Software Engineer
AI research and deployment company building AI scientists and autonomous laboratories for physical-science discovery.
About Periodic Labs
Periodic Labs develops specialized AI models and high-throughput autonomous labs that run and analyze physical experiments, initially for materials discovery including superconductors, magnets, and semiconductor applications.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build reliable backend and distributed systems for scientific work. You will create scheduling and orchestration systems, APIs and data models, durable and observable execution flows, and maintainable production software. You will diagnose failures across software, infrastructure, data, and hardware integrations while improving experimental throughput and reproducibility.
Requirements
- Software engineering fundamentals and experience building production backend or distributed systems
- Experience with asynchronous or long-running work, including batch jobs, workflow orchestration, data pipelines, schedulers, or shared-resource systems
- Knowledge of concurrency, idempotency, partial failures, consistency, and resource contention
- Experience designing maintainable APIs and data models
- Debugging skills across services, infrastructure, dependencies, and data
Responsibilities
- Design and build backend and distributed systems for scientific work
- Build scheduling and orchestration for workflows across compute clusters, instruments, and shared resources
- Create APIs, data models, and service boundaries for research software, laboratory systems, instruments, and automation
- Implement durable state, retries, failure handling, observability, and provenance
- Turn research prototypes and scientific workflows into maintainable production systems
- Diagnose bottlenecks and failures across application code, infrastructure, data, and hardware integrations
- Improve experimental throughput, reliability, and reproducibility with scientists and engineers