About the RoleWe're hiring software engineers to build the backend and distributed systems that form the software backbone of an AI-native physical science lab. These systems turn scientific intent into reliable execution: scheduling large simulation workloads, orchestrating experiments and shared equipment, and connecting instruments and automation to the rest of our software.
Depending on your background and interests, you may work across simulation infrastructure, lab orchestration, or automation systems. You do not need prior experience in every area. We care most about strong engineers who can reason about complex systems, make failure visible, and build software that remains reliable as scientific work scales.
You'll work directly with scientists, infrastructure engineers, and lab engineers to understand how research gets done, then turn prototypes and manual workflows into robust systems without slowing down discovery.
What You'll Do- Design and build backend and distributed systems that execute scientific work reliably.
- Build scheduling and orchestration for long-running workflows across compute clusters, instruments, and other shared resources.
- Create clear APIs, data models, and service boundaries connecting research software, lab information systems, instruments, and automation.
- Make execution inspectable and recoverable through durable state, retries, failure handling, observability, and provenance.
- Turn promising research prototypes and scientific workflows into maintainable production systems.
- Diagnose bottlenecks and failures across application code, infrastructure, data, and hardware integrations.
- Work closely with scientists and engineers to improve experimental throughput, reliability, and reproducibility.
You Will Thrive in This Role If You Have Experience With- Strong software engineering fundamentals and a track record of building production backend or distributed systems.
- Experience with asynchronous or long-running work, such as batch jobs, workflow orchestration, data pipelines, schedulers, or shared-resource systems.
- The ability to reason clearly about concurrency, idempotency, partial failures, consistency, and resource contention.
- Experience designing maintainable APIs and data models for complex systems.
- Strong debugging skills across services, infrastructure, dependencies, and data.
- Comfort working with technical collaborators to turn ambiguous needs into reliable systems.
Especially Strong Candidates May Also HaveYou do not need experience in every area below. Depth in one or more may help us identify where you can have the greatest impact.
- Large-scale batch or distributed compute, scientific simulation, GPU workloads, or ML infrastructure.
- Workflow engines, DAG orchestration, job schedulers, or cluster execution systems.
- LIMS, MES, ERP, process-control systems, lab informatics, or scientific data management.
- Robotics, hardware controls, instrument drivers, vendor APIs or SDKs, or industrial automation.
- Experience in a physical lab, manufacturing environment, or materials-science setting.
MechanicsMinimum education: Bachelor's degree or similar experience
Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)
Compensation: $250,000-350,000 + equity
Visa sponsorship: Yes, we sponsor visas.
