Manager, Forward Deployed Engineer (fde), Life Sciences

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

Manager for Forward Deployed Engineers (FDEs) in Life Sciences at OpenAI. Leads a team delivering production AI systems for drug discovery and development in regulated environments. Acts as a player-coach, responsible for delivery outcomes, team growth, technical direction, and ensuring high-quality, production-grade systems. Success is measured by team health, production adoption, workflow impact, eval-driven feedback, and deployment repeatability. Requires significant engineering and management experience, ideally in life sciences R&D or regulated scientific data.

What you'd actually do

  1. Lead and grow a team of FDEs delivering production AI systems across regulated life sciences environments
  2. Be accountable for your team’s end-to-end delivery outcomes, balancing scope, speed, robustness, and risk in high-stakes deployments
  3. Coach and develop engineers through direct feedback, high technical standards, and clear expectations for execution and ownership
  4. Operate as a player-coach, directly contributing to production systems while leading, coaching, and setting technical direction
  5. Guide teams through ambiguous, multi-workstream engagements spanning data, workflows, infrastructure, security, and scientific stakeholders

Skills

Required

  • 8+ years of engineering or technical delivery experience
  • 2+ years managing high-performing customer-facing or systems-oriented engineering teams
  • Experience leading complex, high-pressure technical programs from prototype through sustained production use in regulated environments
  • Experience working in or adjacent to life sciences R&D, clinical research, scientific software, or regulated scientific data environments
  • Ability to write and review production-grade code
  • Ability to guide architectural decisions across backend, data, and ML-adjacent systems
  • Ability to translate scientific and technical tradeoffs into clear delivery plans, risk posture, and measurable outcomes
  • Ability to elevate team performance through clarity, judgment, and technical credibility
  • Ability to turn field experience into precise, actionable feedback for Product, Research, and GTM teams

Nice to have

  • direct feedback
  • high technical standards
  • clear expectations
  • ambiguous, multi-workstream engagements
  • scientific benchmarks

What the JD emphasized

  • production-grade AI systems
  • regulated environments
  • delivery outcomes
  • evaluation practices
  • production adoption
  • workflow impact
  • repeatability of deployment patterns

Other signals

  • deploying production-grade AI systems
  • regulated environments
  • customer delivery
  • system standards
  • evaluation practices