Forward Deployed Engineer (FDE), Life Sciences - NYC

OpenAI•New York, NY

9d•Hybrid

About The Position

OpenAI’s Forward Deployed Engineering team partners with global pharma and biotech, CROs, and research institutions to deploy existing expertise across the R&D value chain to help customers design and ship production-grade AI systems. 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. We are hiring a Forward Deployed Engineer (FDE) to push the frontier on what is possible today across drug discovery (e.g., target identification, molecular design, pre-clinical) and development (e.g., trial design, trial ops, biostats) by leading end-to-end deployments of our models inside life sciences organizations and research institutions. You will work with customers who are deep experts in their scientific or operational domains, translating real-world data, infrastructure, and constraints into production systems. You will measure success through production adoption, measurable workflow impact, and eval-driven feedback loops, including evaluation benchmarks and acceptance criteria, that inform product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM to deliver in regulated contexts, including inspection readiness with audit trails and traceable evidence. This role is based in NYC. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.

Requirements

Bring 5+ years of software/ML engineering or technical deployment experience with customer-facing ownership in biotech, pharma, clinical research, or scientific software; PhD, MS, or equivalent applied experience in a life sciences relevant field encouraged.
Have owned customer GenAI deployments end-to-end from scoping through production adoption, and improved them through evaluation design, error analysis, and iterative evidence generation that tightens acceptance criteria over time.
Have delivered AI systems in trial design, regulatory writing, or scientific operations where validation strategy, auditability, compliance constraints, and reviewer expectations shaped system design and rollout.
Communicate clearly across scientific, clinical, model research, technical, and executive audiences, translating technical tradeoffs into decision quality, risk posture, and measurable outcomes with credibility.
Apply systems thinking with high execution standards, consistently turning failures, escalations, and audit findings into improved operating standards, validation artifacts, and repeatable deployment playbooks.

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 ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and an execution plan with measurable endpoints.
Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, outcome metrics, and drive delivery until we demonstrate sustained production impact.
Build in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
Run evaluation loops that 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 scale across regulated life sciences environments.