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OpenAI's Life Sciences team is building Rosalind Workbench, a central environment that brings together scientific tools, data sources, interactive biology file viewers, and core life sciences workflows to help researchers accelerate discovery. The platform integrates with GPT-Rosalind, OpenAI's dedicated life sciences model combining frontier reasoning with specialized tool orchestration across medicinal chemistry, genomics, wet-lab assistance, and other scientific domains. The long-term vision is for teams of agents to collaborate across these domains, enabling researchers to pursue more ambitious scientific questions.
As Product Manager for Rosalind Workbench, you will own product strategy and execution, working directly with researchers in academic labs, biotech, and pharma to identify where AI can meaningfully improve their work. You'll partner with engineering, research, design, and customer-facing teams to translate emerging capabilities into intuitive products that scientists adopt and return to regularly.
Key responsibilities include:
- Shape Rosalind Workbench's strategy and roadmap, prioritizing high-impact workflows across discovery biology, verification, and CRO management
- Partner with engineering, research, and design to transform scientific data, tools, and models into intuitive workflows with the depth and control experts require
- Prototype with Codex, Rosalind Workbench, and GPT-Rosalind to validate ideas with scientists before committing engineering resources
- Collaborate with Safeguards, Preparedness, Product Policy, and go-to-market teams to build responsible access into researcher onboarding and product workflows
- Drive enterprise readiness for pharma and biotech, spanning data protection, admin controls, research-system integrations, security, compliance, and procurement
- Own execution from discovery through launch and iteration, translating user needs and technical constraints into clear requirements and driving adoption
- Build scientific rigor into the experience through reproducible analyses, traceable evidence, visible uncertainty, and scientist review
- Use product data, researcher feedback, and market insight to improve scientific utility, reliability, adoption, and business impact
This role offers the opportunity to define the future of AI-guided scientific discovery and build capabilities that help advance the scientific frontier while increasing accessibility of model intelligence for researchers. The company moves with urgency, holds a high bar for quality and trust, and is deeply committed to building products that improve human health outcomes.
REQUIREMENTS:
- Track record of shipping products that solve complex user problems, taking ambitious products from zero to one and scaling adoption or commercial success (founder experience is a plus)
- Substantive life sciences experience through research, industry, or building tools for scientists; ability to discuss experimental design, data quality, and biological interpretation with researchers (advanced degree helpful, but equivalent practical experience matters)
- Strategic judgment combined with hands-on builder mindset: prototype ideas, choose focused use cases, stay close to execution
- Experience building model evaluations or benchmarks, ideally for scientific or agentic workflows, with knowledge of assessing validity, uncertainty, and reliability
- Ability to translate between scientists, engineers, designers, and commercial teams; technical fluency to reason about data and tool integrations, model limitations, and tradeoffs in reliable scientific workflows
- Sound judgment about evidence, uncertainty, privacy, and safety; understanding that convincing output does not equal scientific validity, with ability to build evaluation and expert feedback into product development
- Experience shipping into life sciences, pharma, or regulated research environments with understanding of data governance, compliance, and responsible access to sensitive capabilities
- Deep care for helping scientists make discoveries; curiosity and user empathy combined with high bar for usability and motivation to make advanced AI useful across everyday research realities
