Platform Engineer, Forward Deployed Engineering (fde) -sf

OpenAI OpenAI · AI Frontier · San Francisco, CA · Model Deployment for Business

Platform Engineer within Forward Deployed Engineering (FDE) at OpenAI. This role focuses on building new platform capabilities from scratch, grounded in real customer deployments. The engineer will partner with customer-tagged FDEs to provide hands-on leverage in architecting, product shaping, refactoring, hardening, and creating reusable abstractions. The goal is to translate cross-customer patterns into platform bets and raise the engineering bar through tooling and mentorship, ultimately contributing to shipped software, repeatable patterns, and durable products.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 5+ years of software engineering or ML engineering experience
  • Track record of shipping 0→1 capabilities
  • Experience in high-ambiguity, fast-iteration environments
  • Owned customer-adjacent technical work end-to-end
  • Improved outcomes through structured iteration (instrumentation, evals, error analysis, and tightening success criteria over time)
  • 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
  • Systems thinking

Nice to have

  • Experience in high-ambiguity, fast-iteration environments (startups or product-centric teams) is a plus.

What the JD emphasized

  • shipping 0→1 capabilities
  • customer-adjacent technical work
  • reliability, security, and governance
  • product requirements
  • reusable platform capabilities

Other signals

  • customer deployments
  • shipped software
  • repeatable patterns
  • durable products
  • platform capabilities
  • customer outcomes
  • reusable abstractions
  • generalize
  • product requirements
  • platform bets