Forward Deployed Engineer (FDE), Life Sciences - SF
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, eva
What this role actually needs.
Forward Deployed Engineer (FDE), Life Sciences - SF at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. 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, eva
Day-to-day expectations
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- 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.
What a strong candidate brings
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- 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.
- 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.
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- Use united states residents as part of your positioning so the recruiter does not have to infer it.
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