Browse open Machine Learning Engineer positions aggregated from verified tech companies. Optimize your resume for these roles using our free analyzer and resume examples.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As a Staff Software Engineer on the GTM AI Engineering (Claudification) team, you will build the agents and AI systems that run Anthropic's own go-to-market work. Our sellers already work alongside agents every day. You will take things to the next step and build agents that run complete autonomous motions across areas like inbound, outbound, and pipeline management. In addition, you’ll build eval frameworks that prove those agents are ready for customer-facing work and are driving value. This is a senior role where you’ll drive technical direction for agents and evals across our team. Working closely with sellers, RevOps, and our platform engineering partners, you'll own projects from first prototype through production operation. You'll combine full-stack engineering (MCP servers, agentic systems, web applications, etc.) with hands-on evaluation work (behavior benchmarks, production monitoring, ROI measurement), and help architect the shared platforms that builders from across our go-to-market org contribute to. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing AI engineering function at a pivotal moment in the company's growth. Key responsibilities Build and operate autonomous agents that run go-to-market motions end to end, across areas like inbound, outbound, pipeline management, and customer engagement Design the human oversight for each motion: approval gates, handoffs, and escalation paths that keep sellers in control Develop evaluation frameworks for agent behavior, and run them in development and in production Instrument model and tool calls in production, and build the observability and measurement that ties agent actions to pipeline and revenue Ship MCP servers, agent skills, and web applications that connect to systems like our CRM, communication tools, and data warehouse Set the technical direction for how we build, evaluate, and operate agents across the team Architect shared codebases that builders from across go-to-market contribute to, setting the conventions and review practices that keep quality high Work directly with sellers to ground agent designs in real workflows, and iterate based on what you observe Identify repeatable patterns and contribute insights back to Anthropic's Product and Engineering teams Maintain strong knowledge of the latest developments in LLM capabilities, agent frameworks, and evaluation techniques Minimum qualifications Strong programming skills in Python or TypeScript, with experience building and operating production applications Production experience with LLMs, including context engineering, agent development, MCP development, tool use, and evaluation frameworks Experience using evals and transcript analysis to find and fix real problems in an LLM system Working fluency with data, including SQL Ability to navigate ambiguity and ship without a spec, finding simple solutions to complex problems Passion for advancing safe, beneficial AI, and care for the people who use what you build Preferred qualifications 8+ years in roles such as software engineer, ML engineer, or forward deployed engineer. Former technical founders are encouraged to apply Experience with the Claude Code and the Claude Agent SDK Experience with go-to-market systems (CRM, sales engagement, enrichment, conversation intelligence) or time working closely with a revenue team Experience growing a codebase that many people contribute to, inner-source or open-source Applied ML and experimentation background: A/B testing, propensity models, recommendations, or causal analysis Exceptional communication skills to convey technical concepts to non-technical partners with low ego Representative projects (Illustrative of the kind of work, not a project list.) Build an agent that takes a routine sales workflow from first signal to a drafted, human-reviewed action Stand up the eval suite for an agent: seed scenarios, scoring rubrics, and regression runs on every change Ship an MCP server that gives sellers and their agents governed access to a core revenue system Design a shared repository where go-to-market builders publish agents and skills, with the tests and review rules that keep it healthy Build a predictive model that explains itself, so an agent can tell a seller why it suggests an action The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Engineering Manager, Machine Learning Credit Risk Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies from the world’s largest enterprises to the most ambitious startups use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. Credit risk is a complex machine learning problem that requires us to distinguish emerging risk from healthy business activity while giving legitimate users a clear and reliable experience. Our team consists of machine learning engineers who build models and systems used across Stripe’s credit-risk products. We work closely with partners in Product, Data Science, Credit Strategy, Operations, and other engineering teams. Together, we help stakeholders make informed decisions and support sustainable growth wherever credit risk affects Stripe’s products. What you’ll do We’re looking for an engineering manager to lead the Credit Risk team and shape how Stripe uses machine learning to manage credit risk at scale. You’ll set the team’s technical and product direction, connect advances in machine learning to measurable business outcomes, and help engineers deliver reliable systems that balance loss prevention with the user experience. You’ll work across engineering, product, data science, and risk to identify the highest-impact opportunities and turn them into a focused roadmap. You’ll also hire and develop engineers, strengthen the team’s technical practices, and contribute to machine learning and engineering leadership across Stripe. Responsibilities Set and execute the strategy for detecting and mitigating credit risk through machine learning Own outcomes related to credit losses, profitability, detection quality, and the user experience Lead the design and delivery of reliable machine learning models, services, and decision systems Translate advances in machine learning into practical capabilities that support the team’s business goals Partner with Product, Data Science, Credit Strategy, Operations, and engineering teams to define priorities and deliver cross-functional programs Recruit, hire, and develop machine learning engineers while building an inclusive and effective team Contribute to broader engineering and machine learning initiatives as a member of Stripe’s engineering management team Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 3+ years of experience managing engineers who build and operate production machine learning systems Experience applying machine learning to complex, real-world problems and leading the technical delivery of models and supporting systems Experience setting strategy and working across engineering, product, data science, operations, and business teams to deliver measurable outcomes Experience recruiting, managing, and developing engineers in a fast-moving environment with significant autonomy Preferred qualifications Experience with credit risk, fraud detection, financial risk, trust and safety, or another domain involving decisions under uncertainty Experience balancing risk reduction with customer or user experience Experience building machine learning systems that support high-stakes, time-sensitive decisions at scale Experience setting a multi-year technical direction while delivering progress through quarterly plans Experience managing geographically distributed teams
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences- focusing on raising the floor for those who need it most. About the Role We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R&D through translation. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimagining pharma pipelines - and building the technical infrastructure to back that up. You'll work directly with flagship research partners like The Howard Hughes Medical Institute (HHMI) and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside - you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments Applied AI team focused on bringing life sciences closer to the frontier. Responsibilities Partner deeply with flagship life sciences research institutions - understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day. Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch. Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research. Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding. You Might Be a Good Fit If You Have: Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics. Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks. Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder. A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £225,000 — £255,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. À propos du poste En tant qu'ingénieur·e en IA appliquée au sein de notre équipe Entreprise, votre rôle est de guider certaines des plus grandes entreprises de France vers la frontière de l'IA et de les aider à déployer Claude dans leurs opérations, leurs produits et leurs méthodes de travail. Vous deviendrez le·la spécialiste incontournable de la plateforme Claude et le·la conseiller·ère technique de confiance de vos clients, en conseillant leurs équipes d'ingénierie sur l'architecture, la conception d'agents et l'évaluation, et en travaillant avec les dirigeants et les cadres supérieurs qui sponsorisent ces programmes pour démontrer la valeur ajoutée de Claude et les aider à prendre une décision éclairée quant à l'adoption de Claude. Les transactions avec les entreprises sont différentes. Les cycles d'approvisionnement sont plus longs, davantage de parties prenantes sont impliquées, et la réussite dépend de la capacité à relier le travail technique aux résultats qui importent pour le conseil d'administration. Vous serez tout aussi à l'aise pour conseiller une équipe d'ingénierie sur l'évaluation et l'architecture que pour présenter un dossier de valeur à un CIO ou un CTO, et vous porterez le fil technique à travers chaque étape d'un engagement s'étalant sur plusieurs trimestres. Une grande partie de ce travail se déroule en français, avec des cadres dirigeants et des équipes d'ingénierie françaises, c'est pourquoi un français natif ou courant est essentiel pour ce poste. Vous travaillerez en étroite collaboration avec les responsables de comptes Entreprise et les architectes IA appliquée pour accompagner chaque client depuis la première conversation, à travers l'évaluation technique, la création de valeur et les achats, jusqu'à la décision de développer sur Claude. Une fois leur décision prise, vous préparerez leur partenaire d'intégration système à assurer la mise en œuvre, en définissant ensemble l'architecture cible et les critères de réussite, et vous resterez proche du client en tant que conseiller·ère technique sur Claude pendant tout le déploiement, tandis que le partenaire se chargera de la construction. En travaillant aux côtés des équipes Produit et Ingénierie d'Anthropic, vous mettrez à profit votre connaissance de la plateforme Claude pour conseiller sur l'évaluation, recommander des architectures répondant aux exigences de sécurité et d'échelle des entreprises, et créer les ressources techniques et commerciales qui aident les grandes organisations à réussir avec Claude. Responsabilités Travailler en partenariat avec les responsables de comptes et les architectes en IA appliquée pour accompagner les clients entreprises en France depuis le premier échange jusqu'à la décision d'adopter Claude, en passant par l'évaluation technique, la validation de la valeur et le processus d'achat Piloter le volet technique de chaque opportunité jusqu'à cette décision, et rester le·la conseiller·ère technique du client sur Claude pendant son déploiement Préparer les clients et leurs partenaires d'intégration de systèmes à assurer la livraison : convenir de l'architecture cible, définir les critères de réussite, et rester disponible pour conseiller l'équipe de livraison au fur et à mesure de l'avancement de la construction Guider les clients grandes entreprises vers l'avant-garde de l'IA, en les conseillant sur la manière de déployer Claude dans leurs opérations, leurs produits et leurs méthodes de travail Aider les équipes d'ingénierie des clients à voir comment elles pourraient développer sur Claude, en couvrant l'architecture, la conception d'agents et l'évaluation, afin qu'elles puissent prendre une décision en toute confiance Élaborer et présenter le dossier de valeur commerciale de Claude auprès des dirigeants et des cadres supérieurs, en français et en anglais, en associant les capacités techniques à des résultats mesurables tels que les coûts, les revenus, les risques et le délai de mise sur le marché Guider les clients à travers de longs cycles d'approvisionnement et d'adoption impliquant de multiples parties prenantes, notamment les évaluations techniques, les preuves de concept, les revues de sécurité et d'architecture, jusqu'à la définition d'une feuille de route claire vers la mise en production Travailler avec les responsables de comptes sur la stratégie de compte, la qualification des opportunités commerciales et les plans d'engagement des dirigeants, et avec les architectes en IA appliquée pour assurer la continuité et le partage du contexte à travers chaque engagement Organiser des briefings exécutifs, des ateliers techniques et des sessions de formation destinés à la fois aux dirigeants et aux équipes d'ingénierie Naviguer au sein des exigences de l'entreprise en matière de sécurité, de conformité, de résidence des données et de gouvernance, en collaborant avec les équipes internes d'Anthropic si nécessaire Identifier les modèles à travers les engagements auprès des entreprises et transmettre les informations aux équipes Produit, Ingénierie et à l'équipe IA appliquée élargie Se rendre sur les sites des clients en France pour des réunions de direction, des ateliers et l'établissement de relations Vous conviendrez particulièrement si vous avez Le français comme langue maternelle ou courant, écrit et parlé, car vous travaillerez au quotidien avec des clients entreprises français, aussi bien au niveau des dirigeants qu'au niveau ingénierie Plus de 6 ans d'expérience en tant qu'ingénieur·e logiciel, architecte de solutions, ingénieur·e en déploiement avancé ou fondateur·rice technique Une expérience dans un poste d'avant-vente ou de conseil technique auprès de grandes entreprises, idéalement sur le marché français, y compris la définition du périmètre et la conception de solutions complexes Une expérience dans la vente basée sur la valeur, ce qui signifie que vous êtes en mesure d'élaborer un dossier commercial, de quantifier l'impact et de le présenter à des décideurs de haut niveau L'aisance à présenter et à établir des relations avec des parties prenantes de niveau C et VP Une expérience dans la prise en charge de la réussite technique des transactions avec les entreprises : évaluations, preuves de concept et audits techniques préalables avec les équipes d'ingénierie, de sécurité, juridique et des achats Une expérience en production avec des applications propulsées par des LLM, incluant le prompting, l'ingénierie du contexte, les architectures d'agents, les cadres d'évaluation et le déploiement à grande échelle Une expérience pratique en ingénierie, incluant la conception et le déploiement d'applications en production, afin de pouvoir échanger avec les responsables techniques et leurs équipes Une familiarité avec les exigences en matière de sécurité, de conformité et d'architecture d'entreprise, et une expérience dans la résolution de ces problématiques avec les équipes clientes La capacité à expliquer clairement des concepts complexes d'IA à des publics techniques et dirigeants, et à naviguer entre des secteurs tels que les services financiers, les soins de santé, le commerce de détail et l'industrie manufacturière Un intérêt marqué pour la mise en place de technologies puissantes sûres et bénéfiques About the role As an Applied AI Engineer on our Enterprise team, your role is to guide some of France's largest businesses to the frontier of AI and help them deploy Claude into their operations, their products, and their ways of working. You will become the go-to Claude Platform specialist and trusted technical advisor for your customers, advising their engineering teams on architecture, agent design, and evaluation, and working with the C-level and senior executives who sponsor these programmes to show the business value Claude can create and help them reach a confident decision to build on Claude. Enterprise deals are different. Procurement cycles are longer, more stakeholders are involved, and success depends on being able to connect the technical work to the outcomes the boardroom cares about. You will be as comfortable advising an engineering team on evaluation and architecture as you are presenting a value case to a CIO or CTO, and you will carry the technical thread across every stage of a multi-quarter engagement. Much of this work happens in French, with French executives and engineering teams, so native or fluent French is essential for the role. You will partner closely with Enterprise Account Executives and Applied AI Architects to take each customer from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude. Once they have decided, you set their systems integration partner up to deliver, agreeing the target architecture and what good looks like, and you stay close as the customer's technical advisor on Claude throughout deployment, while the partner does the build. Working alongside Anthropic's Product and Engineering teams, you will use your knowledge of the Claude Platform to advise on evaluation, recommend architectures that meet enterprise security and scale requirements, and create the technical and business resources that help large organisations succeed with Claude. Responsibilities Partner with Enterprise Account Executives and Applied AI Architects to take enterprise customers in France from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude Lead the technical side of each opportunity through to that decision, and remain the customer's technical advisor on Claude as they deploy Set customers and their systems integration partners up to deliver: agree the target architecture, define what good looks like, and stay on hand to advise the delivery team as the build progresses Guide enterprise customers to the frontier of AI, advising them on how to deploy Claude into their operations, their products, and their ways of working Help customer engineering teams see how they would build on Claude, covering architecture, agent design and evaluation, so they can make a confident decision Build and present the business value case for Claude with C-level and senior executives, in French and English, linking technical capabilities to measurable outcomes such as cost, revenue, risk, and time to market Guide customers through long, multi-stakeholder procurement and adoption cycles, including technical evaluations, proofs of concept, security and architecture reviews, and a clear path to production Work with Account Executives on account strategy, deal qualification, and executive engagement plans, and with Applied AI Architects to keep context and continuity across every engagement Run executive briefings, technical workshops, and enablement sessions for both leadership and engineering audiences Navigate enterprise requirements around security, compliance, data residency, and governance, working with Anthropic's internal teams where needed Identify patterns across enterprise engagements and feed insights back to Product, Engineering, and the wider Applied AI team Travel to customer sites across France for executive meetings, workshops, and relationship building You may be a good fit if you have Native or fluent French, written and spoken, as you will work day to day with French enterprise customers at both executive and engineering level 6+ years of experience as a Software Engineer, Solutions Architect, Forward Deployed Engineer, or technical founder Experience in a pre-sales or technical advisory role selling into large enterprises, ideally in the French market, including scoping and designing complex solutions Experience with value-based selling, meaning you can build a business case, quantify impact, and present it to senior decision makers Comfort presenting to, and building relationships with, C-level and VP-level stakeholders Experience owning the technical win in enterprise deals: evaluations, proofs of concept and technical due diligence with engineering, security, legal and procurement teams Production experience with LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale A hands-on engineering background, including building and deploying production applications, so you can relate to engineering leaders and their teams Familiarity with enterprise security, compliance, and architecture requirements, and experience working through them with customer teams The ability to explain complex AI concepts clearly to both technical and executive audiences, and to move between industries such as financial services, healthcare, retail, and manufacturing A passion for making powerful technology safe and beneficial The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: €220.000 — €235.000 EUR Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As an Applied AI Engineer on our Enterprise team, your role is to guide some of Germany's largest businesses to the frontier of AI and help them deploy Claude into their operations, their products, and their ways of working. You will become the go-to Claude Platform specialist and trusted technical advisor for your customers, advising their engineering teams on architecture, agent design, and evaluation, and working with the C-level and senior executives who sponsor these programmes to show the business value Claude can create and help them reach a confident decision to build on Claude. Enterprise deals are different. Procurement cycles are longer, more stakeholders are involved, and success depends on being able to connect the technical work to the outcomes the boardroom cares about. You will be as comfortable advising an engineering team on evaluation and architecture as you are presenting a value case to a CIO or CTO, and you will carry the technical thread across every stage of a multi-quarter engagement. Much of this work happens in German, with German executives and engineering teams, so native or fluent German is essential for the role. You will partner closely with Enterprise Account Executives and Applied AI Architects to take each customer from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude. Once they have decided, you set their systems integration partner up to deliver, agreeing the target architecture and what good looks like, and you stay close as the customer's technical advisor on Claude throughout deployment, while the partner does the build. Working alongside Anthropic's Product and Engineering teams, you will use your knowledge of the Claude Platform to advise on evaluation, recommend architectures that meet enterprise security and scale requirements, and create the technical and business resources that help large organisations succeed with Claude. Responsibilities Partner with Enterprise Account Executives and Applied AI Architects to take enterprise customers in Germany from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude Lead the technical side of each opportunity through to that decision, and remain the customer's technical advisor on Claude as they deploy Set customers and their systems integration partners up to deliver: agree the target architecture, define what good looks like, and stay on hand to advise the delivery team as the build progresses Guide enterprise customers to the frontier of AI, advising them on how to deploy Claude into their operations, their products, and their ways of working Help customer engineering teams see how they would build on Claude, covering architecture, agent design and evaluation, so they can make a confident decision Build and present the business value case for Claude with C-level and senior executives, in German and English, linking technical capabilities to measurable outcomes such as cost, revenue, risk, and time to market Guide customers through long, multi-stakeholder procurement and adoption cycles, including technical evaluations, proofs of concept, security and architecture reviews, and a clear path to production Work with Account Executives on account strategy, deal qualification, and executive engagement plans, and with Applied AI Architects to keep context and continuity across every engagement Run executive briefings, technical workshops, and enablement sessions for both leadership and engineering audiences Navigate enterprise requirements around security, compliance, data residency, and governance, working with Anthropic's internal teams where needed Identify patterns across enterprise engagements and feed insights back to Product, Engineering, and the wider Applied AI team Travel to customer sites across Germany for executive meetings, workshops, and relationship building You may be a good fit if you have Native or fluent German, written and spoken, as you will work day to day with German enterprise customers at both executive and engineering level 6+ years of experience as a Software Engineer, Solutions Architect, Forward Deployed Engineer, or technical founder Experience in a pre-sales or technical advisory role selling into large enterprises, ideally in the German market, including scoping and designing complex solutions Experience with value-based selling, meaning you can build a business case, quantify impact, and present it to senior decision makers Comfort presenting to, and building relationships with, C-level and VP-level stakeholders Experience owning the technical win in enterprise deals: evaluations, proofs of concept and technical due diligence with engineering, security, legal and procurement teams Production experience with LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale A hands-on engineering background, including building and deploying production applications, so you can relate to engineering leaders and their teams Familiarity with enterprise security, compliance, and architecture requirements, and experience working through them with customer teams The ability to explain complex AI concepts clearly to both technical and executive audiences, and to move between industries such as financial services, healthcare, retail, and manufacturing A passion for making powerful technology safe and beneficial Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Who We Are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About The Team The Solutions Architecture (SA) organization helps Stripe's most strategic customers design and validate technical solutions that drive their business forward. Within SA, our team builds the tools, workflows, and custom technical assets that make the broader SA org more effective—turning individual ingenuity into org-wide capability. We are looking for AI Engineers who are energized by working close to the business and the users we serve. You'll operate as an embedded, high-context engineer focused on the highest-leverage opportunities across the SA org—building production-quality tooling, supporting our most strategic engagements, and shipping automation that meaningfully improves how Solutions Architects work every day. What You'll Do As an AI Engineer, you'll be embedded directly with the Solutions Architecture team—building alongside them, deeply understanding their workflows, and shipping tools and automation that permanently change how they operate. Your measure of success is SA productivity: the engagements you've accelerated, the workflows you've transformed, and the tools you've built that the org adopts as default. You'll ship code daily. You'll discover where SAs lose time and build high-impact solutions. You'll take what works for one person and scale it to work for the org. And when our most strategic customer engagements need custom technical assets, you'll build those too. This is a role for someone who wants to work at the intersection of engineering and business impact—close to customers, close to revenue, and building for people you can see using your work every day. Responsibilities Collaborate with Solutions Architects, SA leadership, Product, and Engineering to scope technical work and translate ambiguous business needs into well-defined deliverables Evaluate and integrate AI capabilities (LLMs, agents, workflow automation) where they provide genuine leverage—not for novelty, but for measurable productivity improvement Architect and build internal tools, agents, and automated workflows that accelerate SA and manager productivity across technical discovery, solution design, demoing, user engagements, territory/pipeline management, and product interlock Take high-potential tools and workflows built by SAs and managers and harden them into scalable, maintainable, production-grade solutions Identify patterns across SA workflows and proactively build solutions that address recurring friction Build custom demo environments, PoC applications, and technical assets for Stripe's most strategic customer engagements Document tools, architectures, and usage patterns so others can adopt and extend what you've built Debug, extend, and maintain backend systems across a variety of codebases and infrastructure Minimum Requirements 4+ years of experience as an engineer shipping production systems Strong backend engineering fundamentals: you can debug a failing system, trace issues across services, and reason about data flows Experience building and deploying AI agents, LLM-powered tools, or workflow automation beyond basic prompt engineering Experience scoping and delivering work with minimal oversight in a fast-moving, cross-functional environment Proficiency in at least two of: Ruby, Node.js, Python, or Next.js Familiarity with cloud infrastructure (AWS, GCP) including deployment, monitoring, and basic DevOps Experience building internal tools, developer platforms, or workflow automation Demonstrated ability to work across multiple codebases and technology stacks simultaneously Hands-on experience using AI/LLM tools in your engineering workflow—you're fluent with AI-assisted development but not dependent on it; you can reason through problems and debug without AI as a crutch Strong written and verbal communication skills; you can translate technical decisions for non-technical stakeholders and navigate cross-functional collaboration naturally Comfort with ambiguity—you can take a loosely-defined business problem, scope the engineering work, and ship iteratively without waiting for a perfect spec Preferred Qualifications Experience designing systems that non-engineers can build on top of or extend themselves (e.g., platforms, low-code frameworks, template systems) Experience in a Solutions Engineering, Sales Engineering, or GTM Engineering role—or a product engineering role where you worked closely with customers or go-to-market teams Familiarity with Stripe's products, APIs, or the payments/fintech domain Experience integrating with third-party platforms (Salesforce, Gong, etc.) Track record of building tools or systems that were adopted beyond your immediate team Background in consulting, professional services, or other roles that blend technical depth with business context Who You Are Beyond the technical requirements, we're looking for a specific kind of engineer: You want to be close to the business. You're energized by seeing your work directly impact how a sales team wins a deal or how a customer succeeds. You're a pragmatic builder. You ship working solutions quickly, iterate based on real usage, and know when "good enough now" beats "perfect later." You'd rather show a working prototype today than present a roadmap deck next quarter. You're a software engineer by practice. You can architect systems, debug production issues, write clean code, and reason about tradeoffs. AI is a tool in your belt, not a substitute for engineering judgment. You're a pattern recognizer. When you build something that works for one person, you immediately see how it generalizes. You think in reusable systems, not one-off scripts. You thrive without a traditional product team structure. No PRDs landing in your lap, no dedicated PM, no sprint ceremonies. You identify the highest-leverage problem, scope the work, and ship it. You're comfortable trading on-call rotations and rigid processes for autonomy and impact. You're a strong communicator. You can partner with SAs who are domain experts, understand their workflows deeply enough to build great tools, and explain your technical choices to leadership.
About the Team The Applied AI Engineering team is responsible for helping customers turn frontier AI capabilities into real products, workflows, and business impact. We act as trusted technical partners across solution design, architecture, implementation, evaluation, and adoption, working alongside customers to build and scale effective AI applications with OpenAI’s technologies. The Codex Applied AI Engineering team focuses on helping organizations transform how software is built with AI. We partner directly with engineering teams and technical leaders to integrate Codex into their software development lifecycle — from identifying high-impact use cases and designing AI-enabled workflows to implementation, evaluation, and scaled adoption. Our work helps ensure AI-powered software development is effective, reliable, secure, and deeply integrated into how engineering organizations operate. About the Role We are seeking an experienced technical leader to join as Manager, Applied AI Engineering (Codex) , leading a team of Applied AI Engineers responsible for driving successful Codex adoption across strategic customers. Your team will work hands-on with customer engineering organizations to design and build AI-enabled development workflows, solve complex implementation challenges, and establish scalable patterns for AI-powered software development. As a manager, you will shape how these technical engagements operate at scale — setting strategy, coaching engineers, determining where the team can have the greatest impact, and ensuring consistently strong execution across customers. You will serve as both a people leader and senior technical advisor, partnering closely with Sales, Product, Research, and Engineering to translate customer needs and real-world usage into better technical approaches, reusable patterns, and product insights. Success in this role will be measured by meaningful and sustained Codex adoption, successful customer outcomes, and the creation of repeatable technical patterns that help engineering organizations get more value from AI. This role is open in our Munich and Berlin offices. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In This Role, You Will Lead, hire, and mentor a high-performing team of Applied AI Engineers supporting Codex customers across strategic accounts. Own the operating model and technical engagement strategy for Codex Applied AI Engineering, helping customers move from initial exploration to meaningful, scaled adoption. Guide teams in designing and implementing AI-enhanced development workflows, agentic systems, automations, evaluations, and scalable application architectures. Act as the senior technical escalation point and thought partner for complex customer implementations and engineering challenges. Partner with Sales, Product, Research, and Engineering teams to align customer outcomes with product direction and roadmap priorities. Establish repeatable technical patterns, playbooks, reference architectures, and best practices that enable broader adoption of AI-powered software development. Coach Applied AI Engineers to operate as trusted technical partners to engineering leaders, executives, and highly technical customer teams. Synthesize insights from customer engagements and translate them into actionable feedback for Product, Research, and Engineering. Develop a deep understanding of how customers are using Codex, where they encounter friction, and where new technical approaches or product capabilities could unlock greater impact. Champion safe, reliable, and effective adoption of AI-powered development workflows across industries. You’ll Thrive in This Role If You Have 8+ years of experience in deeply technical, customer-facing roles such as applied engineering, solutions architecture, deployment engineering, technical consulting, or similar roles. Have 2+ years of experience leading technical teams, including hiring, mentoring, and developing engineers. Have experience building or deploying generative AI, developer platforms, agentic systems, or cloud-based software solutions in production environments. Possess strong hands-on software engineering experience and fluency with programming languages such as Python or JavaScript. Understand modern software development lifecycles and have strong intuition for how AI and coding agents can change how software is designed, built, tested, and maintained. Can work directly with customer engineering teams to understand ambiguous problems, develop technical approaches, and turn ideas into working solutions. Are an effective communicator who can translate complex technical and business topics for both engineering teams and executive stakeholders. Thrive in ambiguous, fast-moving environments and enjoy building new operating models, technical patterns, and teams from first principles. Demonstrate strong ownership, humility, technical judgment, and a commitment to helping both customers and teammates succeed. Are personally committed to fostering the safe and beneficial development of AI. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. For software engineering organizations, this means helping customers adopt Codex and other OpenAI capabilities across the software development lifecycle—transforming how teams plan, build, test, review, and deliver software. We work directly with engineering leaders and hands-on developers to identify high-value opportunities, design and implement AI-powered development workflows, and scale what works across engineering organizations. We turn lessons from these deployments into better products, reusable architectures, and technical patterns that help developers everywhere get more value from Codex. About the Role As an Applied AI Engineer focused on Codex, you will partner directly with leading engineering organizations to design, build, and deploy AI systems that transform how software is developed. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from workflow and use-case selection through prototyping, evaluation, production rollout, and scaled adoption. You will work alongside engineering teams to build advanced AI coding workflows, integrations, automations, and evaluation systems—often using Codex itself as part of your development process. You will help customers make technical decisions involving model behavior, agentic workflows, developer environments, security, reliability, evaluation, and operational readiness, while ensuring deployments translate into measurable improvements in engineering productivity and software delivery. You will work closely with OpenAI Product, Research, Engineering, Security, Sales, and the broader Codex organization, translating real-world deployment experience into high-signal product and model feedback. Success is measured by production systems, sustained developer adoption, and meaningful improvements to how engineering organizations build software—not simply successful demonstrations or enablement activity. This role is based in our London office. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Partner directly with engineering leaders and hands-on developers to identify high-value opportunities for Codex and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria. Design, build, and deploy AI-powered software development workflows that improve how engineering teams plan, write, test, review, debug, and deliver software. Work hands-on in code to build prototypes, evaluation harnesses, reference implementations, integrations, workflow automations, and production accelerators—often using Codex as part of your own development process. Help customers progress from promising experiments to reliable production workflows, sustained developer adoption, and scaled impact across engineering organizations. Design systematic approaches for evaluating AI coding systems using representative software engineering tasks, automated graders, production signals, and developer feedback. Make sound technical decisions across models, agents, tools, developer environments, integrations, reliability, observability, latency, cost, safety, security, and operational readiness. Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive technical blockers toward resolution. Lead technical deep dives, workshops, and hands-on enablement that help engineering teams understand and adopt advanced AI coding workflows effectively and safely. Gather high-fidelity insights from real-world Codex deployments and translate them into clear product proposals, model feedback, and technical requirements for OpenAI Product, Research, and Engineering teams. Create reusable architectures, tooling, examples, guides, and technical patterns—including contributions to resources such as the OpenAI Cookbook—that accelerate future Codex deployments. Influence customer engineering strategy by helping technical leaders understand how AI coding systems can reshape their software development lifecycle, engineering practices, and organizational workflows. You’ll thrive in this role if you: Have a demonstrated track record of designing, building, and delivering software or AI systems in enterprise environments, including taking systems from prototype to production. Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, developer tooling, customer engineering, solutions architecture, or technical consulting. Can point to substantial personal contributions in code, architecture, evaluation, debugging, integrations, or production engineering—not only program, enablement, or stakeholder management. Are highly proficient in Python and comfortable working across modern software development environments; experience with JavaScript, TypeScript, or another relevant language is valuable. Are an active user of AI coding tools and have developed a strong point of view on how AI can improve developer productivity and software engineering workflows. Enjoy building high-signal prototypes, integrations, automations, and production solutions that demonstrate and deliver what AI coding systems can enable. Understand how to evaluate AI coding systems systematically, including designing representative tasks, automated evaluations, production signals, and mechanisms for incorporating developer feedback. Have navigated enterprise production requirements such as developer tooling integrations, reliability, observability, security, privacy, data governance, performance, and cost. Can connect technical decisions to developer workflows, adoption, engineering productivity, and measurable business outcomes. Communicate with clarity and credibility across hands-on engineers, engineering leaders, security teams, product leaders, and executives. Are comfortable leading technical workshops and hands-on sessions that help engineering organizations adopt new development workflows and technologies. Bring high agency, strong technical judgment, and end-to-end ownership in ambiguous and rapidly evolving environments. Learn quickly, challenge assumptions constructively, and collaborate with humility; deep prior experience with OpenAI products is not required. Are able to speak English fluently. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
About the team The Partner Applied AI Engineer team ensures the safe and effective deployment of Generative AI applications for developers and enterprises. We act as trusted advisors and technical partners to our customers, helping them build and execute their AI adoption strategy post-sale. Our mission is to develop a strong backlog of GenAI use cases tailored to each customer’s industry and to drive these initiatives from prototype to production through hands-on technical guidance and partnership. As a Partner Applied AI Engineer, you’ll support systems integrators and their most strategic customers transform their business through solutions such as customer service, automated content generation, and novel applications that make use of our newest, most exciting models. About the role We are looking for a driven solutions leader with a product mindset as one og the the founding Partner AIE to own the technical engagement with our systems integrators (including GSIs, RSIs, and boutique SIs) and ensure their customers achieve tangible business value with GenAI. You will help partners identify high-value use cases and provide technical enablement through the implementation of AI solutions. Your efforts will accelerate partners’ time to unlock distribution and adoption, ensuring they deliver exceptional results for our joint customers while maintaining high-quality standards. You will collaborate closely with Sales, Solutions Engineering, Applied Research, and Product teams, and you will report to the Head of Solutions Architecture. This role is based in San Francisco, CA or New York, NY. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Deeply embed with GSIs, RSIs, and boutique SIs as the technical lead, serving as their technical thought partner to ideate and build novel applications on our API for their customers. Work with senior SI and customer stakeholders to identify the best applications of GenAI in their industry and to build/qualify a comprehensive backlog to support their AI roadmap. Intervene directly to accelerate customer time to value through building hands-on prototypes and/or by delivering impactful strategic guidance. Forge and manage relationships with SI and customer stakeholders to ensure the successful deployment and scale of their applications. Codify solution packages and architectural patterns to accelerate time to deployment for customers and partners. Lead technical support during partners' initial projects to ensure successful implementations and mentor their technical teams towards self-sufficiency in delivering OpenAI-powered solutions. Scale the Partner Solutions Architect function by sharing knowledge, codifying best practices, contributing resources to our open-source repositories, and publishing resources to internal and external knowledge bases. Validate, synthesize, and deliver high-signal feedback to the Product and Research teams. You’ll thrive in this role if you: Have 8+ years of technical consulting (or equivalent) experience, managing C-level technical and business relationships with complex global organizations. Have led complex technical projects and programs with many stakeholders, ideally within or through an SI. Have industry experience in programming languages like Python or Javascript. Have led complex implementations of Generative AI and/or traditional ML solutions. Have built and/or delivered prototypes using the OpenAI API. Are an effective presenter and communicator who can translate business and technical topics to all audiences, including senior leaders. Own problems end-to-end and are willing to pick up whatever knowledge you're missing to get the job done. Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Engineering Manager, AI Conversation Platform Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The newly formed Conversation Platform team aims to build a conversation platform for all merchants who use Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include customizing the Stripe landing page to suggest bespoke integrations, allowing users to command the Stripe API in natural language, and resolving user issues automatically. We are developing RAG based systems on the latest LLMs as well as fine-tuning our own models. We’re an end-to-end team going from ideas to models to shipping in production. What you’ll do Responsibilities Driving an ambitious vision for AI/ML that benefits our users Setting the technical & process direction for the team based on business goals Brainstorm and coordinate product integrations with partner teams Proposing new ideas and building prototypes Be an integral part of a larger ML community internally & externally Hire & develop a world-class team to deliver high-quality ML systems. Coach engineers to help them grow in their careers and maintain a high bar Who you are We are looking for ML Engineering Managers who are passionate about using ML to improve products and delight customers. You have experience leading teams that develop streaming feature pipelines, build ML models, and deploy them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action. Minimum requirements Have at least 4 years of experience managing ML teams Experience working as a Machine Learning Engineer, Applied Scientist or equivalent Individual Contributor. Lead by example in high-growth, high-impact, ambiguous environments Have experience building & shipping ML systems. Hold yourself and others to a high bar when working with production systems. Thrive in a collaborative cross-functional environment Preferred qualifications Experience in shipping LLM & RAG systems
Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. Design systems to speed up the time from idea to deployment of new models. Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. Develop pipelines and automated processes to train and evaluate models in offline and online environments. Integrate ML models into production systems and ensure their scalability and reliability. Collaborate with product and strategy partners to propose, prioritize, and implement new product features. Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production. Must have two (2) years of experience in each of the following: ML algorithms and model architectures; Designing, training and evaluating machine learning models; Productionizing and deploying machine learning models at scale; Orchestrating data pipelines and leveraging large-scale datasets; and Building and deploying ML models to solve business problems. Must have one (1) year of experience in each of the following: ML libraries and frameworks including PyTorch, TensorFlow, XGBoost or Spark; and Deep learning, including transformers, test-time compute, or reinforcement learning. Salary: $212,000 - $318,000/yr. This salary range represents the base salary range for the role and any sales commissions / sales bonuses targets, if applicable, would be in addition to the base salary. 40 hrs/week 50% Telecommuting Permitted. Multiple Positions Available. Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. CA29 #LI-DNI
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Our Applied ML team aims to reform how our users interact with Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for our users to sign up and navigate Stripe. We are using the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production. You can learn more about our team’s work from this recent talk . What you’ll do As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community. Responsibilities Our team operates fluidly and here are some problems you may tackle: How do we evaluate a system offline & online? How do we improve performance to match (and beat) humans? How do we ensure model quality doesn’t degrade online? Does fine-tuning an LLM give us better performance? What are the right OSS and in-house platforms we should invest in? And in the process you will: Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product and strategy partners to propose, prioritize, and implement new product features Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions Who you are We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action. Minimum requirements Have at least 3 years of experience shipping ML systems in production Hold yourself and others to a high bar when working with production systems Take pride in taking ownership and driving projects to business impact Thrive in a collaborative environment Preferred qualifications 5+ years of experience in full time software development roles Experience shipping LLM integrations to user products with high quality Experience operating in highly ambiguous environments Knowledge about driving a hypothesis from data
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe. You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that powers Stripe's ML-driven products. Responsibilities Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform. Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy. Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference, large-scale feature stores, real-time monitoring, and LLM and agent orchestration. Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving. Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation. Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions. Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints. Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations related to the end-to-end ML lifecycle. Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company. Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems. Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members. Experience building and operating production ML platform in one or more areas such as model training, model serving, orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency. Strong product instincts and a deep understanding of the business context in which you operate. Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders. The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments. Hands-on experience using AI tools to accelerate how you work. Preferred qualifications Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as distributed training, model inference, feature stores, real-time feature computation, and model registries. Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and model evaluation. Experience rapidly developing prototypes and iterating based on user feedback. Experience training and shipping machine learning models to production to solve critical business problems. Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation). Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI). Ability to synthesize ideas across the organization while setting a compelling technical vision. Comfortable working with geographically distributed teams. Passion for side projects, open source, or self-driven technical initiatives.
About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Risk Detection is focused on providing a delightful experience for our merchants and minimizing friction, while ensuring the safety of our users in the financial ecosystem. Whenever the Risk team takes action on an account, we work to notify Merchants and provide clear status on what’s happening, enable guided workflows for resolving their issues, and redefine our overall Risk processes to make them as smooth as possible for good merchants. What you’ll do We’re looking for an engineering leader to lead and grow a strong team of engineers, build relationships with customers internally and externally, and champion our vision of making Stripe’s risk management a feature that attracts and retains merchants, and becomes a product differentiator. This is an exciting opportunity to partner with teams across Stripe to build the best merchant experience, and contribute directly to Stripe’s growth. Responsibilities Support the team in delivering a high level of technical quality and impact via APIs, user-facing experiences, services, and systems Recruit, hire, scale, and develop an amazing team of engineers Executing cross-functionally with leadership, product teams, infra teams & risk strategists Be actively involved in strategic direction and platform decisions that impact all of Stripe and Stripe customers Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements At least 3 years of engineering management experience Prior experience as a Machine Learning Engineer or equivalent Preferred qualifications You have managed teams that can collaborate with product teams, respond rapidly to customer needs along with building technology and capabilities that are strategic and foundational in nature Enjoy designing, measuring & improving user experience You are empathetic to customer needs but visionary enough to not just deliver a faster horse You are comfortable planning in quarters, and can set a vision for several years You are comfortable working with geographically distributed teams and remote workers
Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions Mentor engineers and contribute to a strong ML engineering culture within the team Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of industry experience building and shipping ML systems in production Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark Hands-on experience in designing, training, and evaluating machine learning models Hands-on experience in productionizing and deploying models at scale Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets Strong collaboration skills and the ability to work across teams and contribute to peers' success Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset Preferred qualifications MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science) Experience in fintech, open banking, or financial data domains Experience with NLP, LLMs, or text classification at scale Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience with deep learning architectures, including transformers
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe. You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that powers Stripe's ML-driven products. Responsibilities Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform. Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy. Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference, large-scale feature stores, real-time monitoring, and LLM and agent orchestration. Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving. Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation. Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions. Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints. Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations related to the end-to-end ML lifecycle. Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company. Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems. Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members. Experience building and operating production ML platform in one or more areas such as model training, model serving, orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency. Strong product instincts and a deep understanding of the business context in which you operate. Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders. The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments. Hands-on experience using AI tools to accelerate how you work. Preferred qualifications Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as distributed training, model inference, feature stores, real-time feature computation, and model registries. Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and model evaluation. Experience rapidly developing prototypes and iterating based on user feedback. Experience training and shipping machine learning models to production to solve critical business problems. Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation). Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI). Ability to synthesize ideas across the organization while setting a compelling technical vision. Comfortable working with geographically distributed teams. Passion for side projects, open source, or self-driven technical initiatives.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team You will be joining Stripe’s ML Foundations and Gen AI team to incubate new ML applications and improve our ML capabilities across Stripe. Our team is responsible for unlocking novel ML and LLM techniques and applications across Stripe’s product suite to drive business outcomes, as well as providing infrastructure, tooling and support for ML teams. What you’ll do As a senior product leader, you will lead a cross-functional team to define, incubate and scale new ML/AI applications across Stripe’s product suite, and drive our strategy and roadmap for ML/AI infrastructure powering all of Stripe’s teams. You will work closely with product leaders across business units to define and deliver on an AI-centric product strategy, launching new applications that drive incremental business outcomes. At the same time, you will be advancing our core AI technology stack to empower teams across Stripe to infuse their scenarios with Agents and agentic capabilities, with API support for agent quality and continuous improvement. Responsibilities Develop and execute on the Stripe-wide strategy for new ML/AI applications across our product suite Evaluate and align on areas of investment for ML/AI applications in collaboration with product leaders across the company Work with cross-functional teams to execute on the roadmap and launch successful new ML/AI applications Communicate clearly and crisply with leadership stakeholders and drive alignment across multiple teams Develop and execute on a strategy for advancing Stripe’s ML/AI infrastructure and tooling Who you are We’re looking for someone who meets the requirements below, and has a passion for AI to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 7+ years of experience delivering highly successful and innovative software products which are ML powered Solid understanding of ML and applied AI tech stacks Demonstrated ability to influence company level strategy and work with business leaders to execute on the transformation You push the pace. You take blame and pass the praise. People love working with you. Proven ability to lead teams and work cross-functionally in a highly collaborative environment. Ability to analyze and use quantitative and qualitative data to inform decisions. A deep understanding and empathy for consumer and business users — you love building products that make our customers feel joy, delight and trust. Relentlessly drives product quality Capable of working on both 1P and 3P products