Platform Engineer, Forward Deployed Engineering

OpenAI•San Francisco, CA

20h•Hybrid

About The Position

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 product direction. This group is an innovation loop within FDE. We sprint on a small number of platform bets at a time: identify high-signal problems emerging across deployments, build early versions quickly, validate them with design partners, and work with our core engineering and product counterparts to put successful bets on a scalable path. When a capability is ready to scale, we help transition ownership — and then move on to the next frontier. Platform Incubation 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’ll join a small team working in pods against platform bets rather than a single long-running account. You’ll collaborate closely with customer-tagged FDEs and partner teams across engineering and product to: (1) incubate early versions, (2) validate them with design partners through short deployments or pilots, and (3) drive adoption by making the capability durable, usable, and ready to scale. You should expect to be customer-facing when it matters – pitching, rollout planning, debugging, and learning directly from what breaks in production – but your primary charter is to turn frontier signal into reusable platform capability. 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 <10%, with occasional spikes for key embeds or launches.

Requirements

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.

Responsibilities

Architect and build new platform capabilities: turn frontier customer signal into concrete designs, implementations, and APIs that become part of the OpenAI platform.
Incubate platform bets end-to-end: take ambiguous problems, form a crisp hypothesis, ship an initial capability, and iterate quickly based on what breaks in real usage.
Embed with design partners to learn fast: get close to production constraints, run deep technical discovery, and translate needs into product and platform requirements.
Partner with customer-tagged FDEs in the field: deploy and debug together, capture repeatable patterns, and convert field learnings into platform improvements.
Design and run pilot programs: define qualification criteria for early adopters, stand up internal/external alphas, and use pilots to harden both the platform and the rollout playbook.
Collaborate as part of cross-functional platform teams: partner closely with core product and engineering counterparts, often as a single virtual team, to align on architecture and get to production together.
Drive adoption outcomes: measure usage, identify blockers and failure modes, and prioritize the next platform increments that unlock repeatable value.