Manager, Forward Deployed Engineer (FDE), Life Sciences
Application analysis
Manager, Forward Deployed Engineer (FDE), Life Sciences at OpenAI is a forward deployed ai research lab role for engineers who want customer-facing technical work with real deployment ownership.
Reading this role
This Forward Deployed Engineer role centers on embedded customer delivery — discovery, prototyping, integration, and production rollout. Key signals from the listing: Senior scope.
About OpenAI
OpenAI ships frontier models to real customers through small, high-trust engineering teams. Forward deployed roles sit between product, research, and enterprise buyers. The role is anchored in San Francisco, so expect on-site customer work in that region.
Signals in the listing
Stand out for Manager, Forward Deployed Engineer (FDE), Life Sciences
- Lead your application with one paragraph tying Manager, Forward Deployed Engineer (FDE), Life Sciences to a specific customer or deployment you owned — OpenAI hires for shipped outcomes, not tool lists.
- Prepare a 5-minute “week one on site” plan: who you would meet, what you would inspect in their stack, and what artifact you would deliver by day five.
- Show you can go from discovery notes → working integration → production checklist without needing a large internal team.
- This role is tied to San Francisco — mention willingness to work on-site with customers and any existing ties to that market (clients, partners, or domain knowledge).
Stand out at OpenAI
- Demonstrate customer obsession with safety constraints with a concrete example — OpenAI will probe for specifics, not buzzwords.
- Demonstrate writing and communication with a concrete example — OpenAI will probe for specifics, not buzzwords.
- Interview loops emphasize problem decomposition, taste in product, and whether you can earn trust with technical and non-technical stakeholders quickly.
- Read recent engineering blog posts, product launches, and customer stories on openai.com — reference one explicitly in your outreach to show genuine interest.
Tailored from this listing’s title, company, and description. Always verify requirements on the company careers page before applying.
Role description
About the team
OpenAI’s Forward Deployed Engineering (FDE) team partners with global pharma and biotech, CROs, and research institutions to deploy production-grade AI systems across the R&D value chain. We operate at the intersection of customer delivery and core platform development, converting early deployments into repeatable system standards and evaluation practices that scale across regulated environments.
About the role
As a Life Sciences FDE Manager, you’ll lead a team of FDEs delivering production AI systems across drug discovery and development workflows. You’ll own delivery outcomes and team leverage while staying hands-on as a player-coach. This includes building and shipping alongside the team, setting technical direction, and maintaining a high bar for production-grade systems in regulated environments. We measure success through the health and quality of your FDE team, production adoption and measurable workflow impact, the quality of eval-driven feedback delivered back to Product and Research, and the repeatability of deployment patterns across life sciences customers. This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. This role will require travel up to 25%. In this role you will - Lead and grow a team of FDEs delivering production AI systems across regulated life sciences environments - Be accountable for your team’s end-to-end delivery outcomes, balancing scope, speed, robustness, and risk in high-stakes deployments - Coach and develop engineers through direct feedback, high technical standards, and clear expectations for execution and ownership - Operate as a player-coach, directly contributing to production systems while leading, coaching, and setting technical direction - Guide teams through ambiguous, multi-workstream engagements spanning data, workflows, infrastructure, security, and scientific stakeholders - Run evaluation loops that measure model and system qualit