Platform Engineer, Forward Deployed Engineering (FDE) - NYC

About the team OpenAI’s Forward Deployed Engineering (FDE) org sits at the intersection of product, engineering, research, and go-to-market. We take frontier platform capabilities into the real world with design partners, turning raw customer signal into shipped software, repeatable patterns, and durable products. The FDE Platform team is primarily a leverage function that scales the FDE org’s impact to OpenAI’s platform and products. We provide hands-on leverage by embedding with customer-tagged FDE pods to aid in architecting, product shaping, refactoring, and building. This team is perfect for highly collaborative software engineers who love innovating on cutting-edge products with other builders. About the role Platform Engineer is a role within Forward Deployed Engineering (FDE) for strong software and ML engineers who want to build new platform capabilities from scratch, grounded in real customer deployments. You will partner with customer-tagged FDEs who are driving delivery and customer outcomes, and embed where you can provide the highest leverage. In practice that means working in the trenches on architecture, product shaping, refactoring, hardening, and reusable abstractions, while preserving the pod’s ownership of customer understanding and day-to-day execution. You will also collaborate closely with our B2B Platform Team and other long-term owners to align early on what should generalize, what should remain customer-specific, and what “ready for handoff” looks like. This role does not require travel. It is based in San Francisco or New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel is optional-by-project and typically In this role you will - Provide hands-on leverage to customer pods: embed with customer-tagged FDE teams to support generalization, contributing directly in architecture, product shaping, refactoring, and implementation. - Turn repeated signals into platform bets: translate cross-customer patterns into crisp hypotheses with clear success criteria, scope, and a validation plan that fits real account constraints. - Raise the engineering bar through tooling and mentorship: set org-wide quality norms through high-signal code review and pairing, and build lightweight developer tooling that makes good architecture, readability, and correctness the default across FDE. - Collaborate as part of cross-functional platform teams: partner closely with B2B Product, customer-tagged FDEs, ops, and business partners to bring the right products and platform capabilities to market. - Lead complex platform capabilities end-to-end when needed: for high-leverage primitives like our Context Platform, act as DRI from requirements through implementation, make key tradeoffs explicit, and pull in customer pods early to keep the work grounded in real deployments. You might thrive in this role if you - Bring 5+ years of software engineering or ML engineering experience with a track record of shipping 0→1 capabilities that other engineers or customers depend on. Experience in high-ambiguity, fast-iteration environments (startups or product-centric teams) is a plus. - Have owned customer-adjacent technical work end-to-end, from scoping and hypothesis-setting through production adoption, and improved outcomes through structured iteration (instrumentation, evals, error analysis, and tightening success criteria over time). - Have built or operated systems where reliability, security, and governance materially shaped design (permissions/RBAC, auditability, data access boundaries, rollout safety, observability, and incident-driven hardening). - Communicate clearly across engineering, product, go-to-market, and executive audiences, simplifying complex ideas and translating technical tradeoffs into adoption impact, sequencing decisions, and measurable outcomes. You can credibly “pitch” a platform bet in a customer conversation. - Default to systems thinking: you turn ambiguous feedback, failures, and escalations into durable product requirements and reusable platform capabilities, not one-off fixes or bespoke delivery work. 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 Affirmativ

5+ years of software engineering or ML engineering experience with a track record of shipping 0→1 capabilities that other engineers or customers depend on. Experience in high-ambiguity, fast-iteration environments (startups or product-centric teams) is a plus. Have owned customer-adjacent technical work end-to-end, from scoping and hypothesis-setting through production adoption, and improved outcomes through structured iteration (instrumentation, evals, error analysis, and tightening success criteria over time). Have built or operated systems where reliability, security, and governance materially shaped design (permissions/RBAC, auditability, data access boundaries, rollout safety, observability, and incident-driven hardening). Communicate clearly across engineering, product, go-to-market, and executive audiences, simplifying complex ideas and translating technical tradeoffs into adoption impact, sequencing decisions, and measurable outcomes. You can credibly “pitch” a platform bet in a customer conversation. Default to systems thinking: you turn ambiguous feedback, failures, and escalations into durable product requirements and reusable platform capabilities, not one-off fixes or bespoke delivery work.

Provide hands-on leverage to customer pods: embed with customer-tagged FDE teams to support generalization, contributing directly in architecture, product shaping, refactoring, and implementation. Turn repeated signals into platform bets: translate cross-customer patterns into crisp hypotheses with clear success criteria, scope, and a validation plan that fits real account constraints. Raise the engineering bar through tooling and mentorship: set org-wide quality norms through high-signal code review and pairing, and build lightweight developer tooling that makes good architecture, readability, and correctness the default across FDE. Collaborate as part of cross-functional platform teams: partner closely with B2B Product, customer-tagged FDEs, ops, and business partners to bring the right products and platform capabilities to market. Lead complex platform capabilities end-to-end when needed: for high-leverage primitives like our Context Platform, act as DRI from requirements through implementation, make key tradeoffs explicit, and pull in customer pods early to keep the work grounded in real deployments.