Forward Deployed Engineer (FDE), Life Sciences - NYC

OpenAI•New York, NY

2d•$185,000 - $300,000•Hybrid

About The Position

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

Requirements

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, and improving them through evaluation design, error analysis, and iterative evidence generation.
Clear communication across 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 turning 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.
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.

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 execution plans with measurable endpoints.
Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, and outcome metrics, and drive delivery until sustained production impact is demonstrated.
Build in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
Run evaluations of 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.