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Periodic Labs

Software Engineer

Menlo ParkfulltimemidAdded 1 month ago

About this role

Periodic Labs seeks a senior full-stack software engineer to build orchestration systems for an automated materials synthesis lab. You'll design workflow scheduling, instrument coordination, and data provenance platforms that keep scientific experiments running reliably at scale, working across Python, React, and cloud infrastructure.

What you'll do

  • Own and evolve the lab orchestration platform handling instrument scheduling, workflows, and end-to-end sample tracking
  • Build workflow orchestration for multi-step synthesis pipelines with DAG execution, dependency resolution, and retry logic
  • Design instrument scheduling systems managing resource contention, prioritization, and batching across shared equipment
  • Implement complete data provenance tracking ensuring full traceability of samples, actions, and results
  • Develop React interfaces for scientists and operators to monitor experiment state, queue status, and system health
  • Collaborate with infrastructure and lab teams to maintain reliability as instrument count and throughput scale

What they're looking for

  • Production MES, LIMS, ERP, or process control systems
  • Workflow and DAG orchestration design
  • Concurrent systems and resource scheduling
  • Data provenance and event sourcing architecture
  • Full-stack development (Python, React, cloud infrastructure)
  • Scheduling algorithms for constrained resources
  • Lab/manufacturing environment experience
  • Kubernetes and cloud-native infrastructure
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Periodic Labs

Periodic Labs builds AI systems and autonomous lab infrastructure to accelerate materials discovery and scientific research, integrating machine learning with physical lab workflows and quantum simulation. The company is hiring for infrastructure engineers, product engineers, research engineers, and deployment specialists who bridge AI models with materials scientists and lab automation systems.

View all jobs at Periodic Labs

Likely interview questions

  • Walk us through a production system you've built that required complete data provenance and auditability. How did you model the data, and what tradeoffs did you make?
  • Describe your experience with workflow orchestration or DAG execution. Have you built retry logic, dependency resolution, or recovery mechanisms for long-running processes?
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