# Manager, Forward Deployed Engineer (FDE), Life Sciences

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Engineering Management
**Salary:** $252k – $335k/yr
**Experience:** 8+ years
**Skills:** ai systems, backend, data, Machine Learning, Python, Evaluation Frameworks, regulated environments, life sciences r&d, scientific software, Infrastructure, Security
**Posted:** 2026-07-22

> Lead a team of Forward Deployed Engineers to deliver and scale production AI systems for life sciences R&D and clinical workflows at OpenAI. Requires 8+ years engineering experience (2+ years managing), hands-on coding in regulated environments, and strong stakeholder communication.

## Job Description

## Responsibilities
- Lead and grow a team of Forward Deployed Engineers (FDEs) delivering production AI systems across regulated life sciences environments.
- Be accountable for the 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.

## Requirements
- 8+ years of engineering or technical delivery experience, including 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 and 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 across scientific, clinical, technical, and executive audiences.
- 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-Haves
- Experience in regulated environments (e.g., pharma, biotech, CROs, research institutions).
- Hands-on with evaluation frameworks and converting results into product roadmaps.

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