Forward Deployed Engineer (fde) - Sf

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

This role focuses on leading end-to-end deployments of frontier AI models for strategic customers, bridging customer needs with core platform development. The FDE will own the entire delivery lifecycle, from discovery and scoping to system design, build, and production rollout, ensuring measurable workflow impact and gathering feedback to influence product and model roadmaps. The role involves building full-stack systems, embedding with customer teams, and contributing code directly when necessary, with a strong emphasis on making trade-offs between scope, speed, and quality.

What you'd actually do

  1. Own technical delivery across multiple deployments from first prototype to stable production
  2. Build full-stack systems that deliver customer value and sharpen how we learn
  3. Embed closely with customer teams, understand their needs, and guide adoption of what you build
  4. Scope work, sequence delivery, and remove blockers early
  5. Make trade-offs between scope, speed, and quality; adjust plans to protect delivery

Skills

Required

  • 5+ years of engineering or technical deployment experience
  • Customer-facing work experience
  • Experience scoping and delivering complex systems in fast-moving or ambiguous environments
  • Production-grade code development (Python, JavaScript, or comparable)
  • Experience building or deploying systems powered by LLMs or generative models
  • Ability to simplify complexity and make fast, sound decisions under pressure
  • Clear communication with engineers, product teams, and customer stakeholders
  • Risk spotting and proactive adjustment
  • Calmness and judgment under high stakes

Nice to have

  • Understanding of how model behavior affects product experience

What the JD emphasized

  • production systems
  • frontier models
  • end-to-end deployments
  • customer delivery
  • measurable workflow impact
  • eval-driven feedback

Other signals

  • customer delivery
  • production systems
  • frontier models
  • end-to-end deployments
  • measurable workflow impact
  • eval-driven feedback