The opportunity
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…
What you'll do
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.
Coordinate across teams and partners. Align with Codex on shared platform: capabilities and with customer-facing engineers on deployment needs. Lead technical decisions about building or integrating scientific tools, specialist models, data sources, and experimental service providers.
What they're looking for
- Turn customer learning into reusable improvements. Work directly with: scientists and design partners to understand their workflows. Address enterprise requirements such as private compute, credentials, networking, permissions, and data handling, with clear ownership for common product improvements and customer-specific integrations.
- Connect product engineering with research. Help turn capabilities proven in: internal research into supported product features. Build instrumentation and feedback pipelines that measure successful workflows and repeat use, and support evaluation and model improvement under explicit consent and data-use requirements.
- Build safety and trust into the product. Partner with security, privacy, and: safety teams to implement appropriate access controls, review points, auditability, and retention policies throughout scientific workflows.
- Have experience managing engineering teams that ship and operate complex software products.
