Software Engineer
Technologies
Python · React · Kubernetes · DAG orchestration · event sourcing · CQRS · MES · LIMS
About Periodic Labs
AI scientists running autonomous robotic labs that synthesize and characterize materials, training models on real-world experimental feedback to accelerate discovery.
Seed · 50–100 people
Job description
The full responsibilities and requirements are on the employer’s site.
Open application page ↗Source and classification
Internal deployment & tooling · Evidence for this classification:
About Periodic Labs We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible. About the Role We’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
More from the job description
About Periodic Labs We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible. About the Role We’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 sys [... source excerpt omitted ...] ble 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 orchestratio
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