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OpenAI's Life Sciences team is building platforms that connect frontier AI model capabilities with practical scientific workflows. The team works across the full spectrum of life sciences—from next-gen sequencing analysis to drug design—helping scientists leverage AI to accelerate research and access laboratory services.
You will join as a full-stack product engineer with substantial ownership of projects. Your responsibilities include:
- Design backend services, APIs, and integrations that connect users with AI models, scientific tools, and laboratory services
- Create intuitive interfaces that help scientists navigate complex workflows
- Own projects end-to-end: from problem definition and technical design through launch, operation, and iteration
- Work with scientific and research partners to identify user needs and prioritize product improvements
- Partner with infrastructure engineers on performance, reliability, and scaling
- Make thoughtful engineering decisions balancing speed, quality, and long-term maintainability
You should have strong software engineering fundamentals with depth in backend development and the ability to build across the full stack. You've shipped and operated products used by large user bases and bring strong product sense—the ability to translate ambiguous needs into useful software. You work autonomously, make sound technical decisions, and drive projects forward with limited direction. You enjoy hands-on building and taking ownership within a lean team, and you're excited about how advances in AI could transform scientific work.
You learn unfamiliar domains quickly and collaborate effectively with scientific and technical partners. While life sciences experience is helpful, you don't need a scientific background to make a meaningful contribution.
Particularly relevant experience includes: building AI applications or model-serving integrations; developing marketplaces or products that coordinate services and complex workflows; taking new products from early concepts through launch and adoption; working with scientific tools, research data, or laboratory systems.
