Engineering Manager, Rosalind Workbench

OpenAI•San Francisco, CA

4d•$401,000 - $536,000

About The Position

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. 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.

Requirements

Experience managing engineering teams that ship and operate complex software products.
Strong software engineering fundamentals and the technical judgment to work across user-facing applications, backend services, data systems, and compute infrastructure.
Experience helping turn early prototypes or research capabilities into reliable products used by external customers.
Ability to create clarity in ambiguous situations, communicate technical decisions plainly, and coordinate teams with different priorities and reporting structures.
Care deeply about product usability and can translate expert workflows into software that users can understand, inspect, and trust.
Ability to build teams where engineers take ownership, learn quickly, and maintain high standards while shipping.
Motivation by scientific discovery and willingness to learn closely from scientists and domain experts.

Nice To Haves

Scientific software, computational biology, bioinformatics, chemistry, or laboratory workflows.
AI agents, model inference, tool integrations, or evaluation infrastructure.
Workflow orchestration, long-running jobs, reproducible computation, or artifact versioning.
Enterprise software deployed with private data, customer-managed infrastructure, and organizational access controls.
Developer platforms that let users build, validate, and share reusable tools or workflows.

Responsibilities

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.
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.