Engineering Manager, Rosalind Workbench

ABOUT THE TEAM

Life sciences is one of the clearest areas where advances in intelligence can meaningfully benefit the world at large. The OpenAI Life Sciences team works at the intersection of advancing frontier life sciences model capabilities and building products to help scientists accelerate their research and leverage the full potential of AI for scientific work.

Rosalind Workbench brings together the scientific tools, data sources, interactive biology file viewers and core life sciences workflows into a central environment to help scientists investigate questions, design experiments, analyze results, and advance discovery. Rosalind Workbench can be used with any OpenAI models including GPT-Rosalind, our dedicated life sciences model, which combines frontier reasoning with specialized tool orchestration across medicinal chemistry, genomics, wet-lab assistance, and other scientific applications. Our long term vision is for teams of agents to work together across these domains, giving researchers access to broader expertise and the ability to pursue more ambitious scientific questions.

ABOUT THE ROLE

We’re looking for an Engineering Manager to lead the team building Rosalind Workbench and its supporting infrastructure. You’ll hire and develop engineers, set technical direction, and turn an ambitious product roadmap into focused, reliable releases.

This is a hands-on leadership role. You should be comfortable reviewing architecture and code, investigating difficult failures, and helping engineers make consequential technical decisions. You’ll work closely with product, design, research, Codex engineering, and customer-facing teams to bring new scientific capabilities into everyday use.

IN THIS ROLE, YOU WILL

- Build and lead the engineering team. Hire strong engineers, coach technical leaders, provide clear feedback, and establish ownership and accountability across product and infrastructure work.

- Drive technical planning and delivery. Translate scientific and customer needs into scoped milestones. Make explicit tradeoffs, resolve dependencies, and keep a small team focused as the product and research agenda evolve.

- Shape the Workbench architecture. Guide development across scientific interfaces, agent and tool orchestration, local and remote compute, durable execution, and persistent project state. Build shared components that support new scientific workflows.

- Make scientific work inspectable and reproducible. Preserve the inputs, versions, results, and decisions behind an analysis. Ensure scientists can move between conversations, viewers, and follow-up investigations without losing context or control.

- Own engineering quality in production. Establish testing, release practices, observability, and an effective on-call process. Improve workflow reliability, latency, and cost, and lead resolution of problems that prevent scientists from completing their work.