About the roleAs 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 quality against workflow-specific scientific benchmarks, then convert results into crisp roadmap input
You might thrive in this role if you- Bring 8+ years of engineering or technical delivery experience, including 2+ years managing high-performing customer-facing or systems-oriented engineering teams
- Have led complex, high-pressure technical programs from prototype through sustained production use in regulated environments
- Have experience working in or adjacent to life sciences R&D, clinical research, scientific software, or regulated scientific data environments
- Write and review production-grade code and can guide architectural decisions across backend, data, and ML-adjacent systems
- Translate scientific and technical tradeoffs into clear delivery plans, risk posture, and measurable outcomes across scientific, clinical, technical, and executive audiences
- Elevate team performance through clarity, judgment, and technical credibility
- Turn field experience into precise, actionable feedback for Product, Research, and GTM teams
