Forward Deployed Engineer (FDE), Life Sciences - SF

OpenAI•San Francisco, CA

2d•$185,000 - $300,000•Hybrid

About The Position

OpenAI’s Forward Deployed Engineering team partners with global pharma and biotech companies, CROs, and research institutions to deploy AI expertise across the R&D value chain. The team works at the intersection of customer delivery and core platform development, turning early deployments into repeatable system standards and evaluation practices for regulated environments. The Forward Deployed Engineer will lead end-to-end deployments of OpenAI models within life sciences organizations and research institutions, advancing applications across drug discovery—including target identification, molecular design, and pre-clinical work—and drug development, including trial design, trial operations, and biostatistics. The role translates scientific and operational needs, real-world data, infrastructure, and constraints into production systems. Success is measured through production adoption, measurable workflow impact, and evaluation-driven feedback that informs product and model roadmaps. The engineer collaborates with Product, Research, Partnerships, GRC, Security, and GTM teams to deliver in regulated contexts, including inspection readiness supported by audit trails and traceable evidence. The position is based in San Francisco and follows a hybrid model requiring three days in the office each week. Relocation assistance is offered, and travel of up to 50% is required.

Requirements

At least 5 years of software/ML engineering or technical deployment experience with customer-facing ownership in biotech, pharma, clinical research, or scientific software.
Experience owning customer GenAI deployments end-to-end, from scoping through production adoption.
Experience improving deployments through evaluation design, error analysis, and iterative evidence generation that progressively tightens acceptance criteria.
Experience delivering AI systems in trial design, regulatory writing, or scientific operations where validation strategy, auditability, compliance constraints, and reviewer expectations shaped system design and rollout.
Ability to communicate clearly with scientific, clinical, model research, technical, and executive audiences, translating technical tradeoffs into decision quality, risk posture, and measurable outcomes.
Systems thinking and high execution standards, including the ability to turn failures, escalations, and audit findings into improved operating standards, validation artifacts, and repeatable deployment playbooks.

Nice To Haves

PhD, MS, or equivalent applied experience in a life sciences-relevant field.

Responsibilities

Design and ship production systems around models, owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows.
Lead discovery and scoping from pre-sales through post-sales, translating workflow needs into hypothesis-driven problem framing, system requirements, and execution plans with measurable endpoints.
Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, and outcome metrics, and drive delivery through sustained production impact.
Build systems in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
Run evaluation loops to measure model and system quality against workflow-specific scientific benchmarks, and use results to drive model and product changes.
Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that can scale across regulated life sciences environments.