Forward Deployed Engineer (FDE), Life Sciences - NYC

About the team

OpenAI’s Forward Deployed Engineering team partners with life sciences organizations to deploy production AI systems across scientific and operational workflows. We work at the boundary of customer deployment and core platform development, using early engagements to define repeatable system patterns, evals, and operating standards for life sciences environments.

About the role

We are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of our models inside life sciences organizations and research institutions, focusing on workflows across discovery, clinical development, submissions, and scientific operations. You will work with customers who are experts in their domains, translating data, infrastructure, and workflow constraints into production systems and helping define how frontier models can be applied in regulated environments.

You will measure success through production adoption, workflow impact, and eval loops that define workflow-specific benchmarks, acceptance criteria, and launch evidence for production use. You’ll work closely with Business, Research, Platform/Product, Engineering, and Security/GRC, using deployment learnings to improve both customer systems and the product and model roadmaps supporting them.

This role is based in New York City. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 30% is required.

In this role you will

- Own deployments from initial scoping through production adoption, including technical decisions, sequencing, and launch readiness.

- Partner with customers and internal teams to frame problems, define scope, and translate ambiguous workflow needs into system requirements and measurable endpoints.

- Define launch criteria for regulated contexts, including validation evidence, outcome metrics, and acceptance thresholds tied to production use.

- Enforce operating standards for auditability, traceability, and inspection readiness in the systems you ship.

- Design evals that measure model and system quality against workflow-specific scientific benchmarks and acceptance criteria.

- Use evaluation results, error analysis, and deployment learning to improve model selection, system design, and product feedback.

- Distill deployment learnings into reference architectures, validation templates, benchmark harnesses, and other technical primitives that can be reused across life sciences environments.

You might thrive in this role if you

- Bring 6+ years of software, ML/AI, or deployment engineering experience with customer-facing ownership in biotech, pharma, clinical research, scientific software, or adjacent technical domains.

- Have operated as a senior engineer, tech lead, or deployment owner who is trusted to make technical decisions in ambiguous environments.

- Have owned customer GenAI deployments end-to-end from scoping through production adoption.

- Have improved deployed systems through eval