We are an AI + physical sciences lab building state of the art models to make novel scientific discoveries. We are well funded and growing rapidly. Team members are owners who identify and solve problems without boundaries or bureaucracy. We eagerly learn new tools and new science to push forward our mission.
About the RoleAt Periodic Labs, our scientists don’t just design experiments — they direct an automated materials synthesis lab that runs around the clock. Behind that lab is a system that has to work: scheduling dozens of instruments, tracking every sample from precursor to characterization, and orchestrating multi-step synthesis pipelines without dropping a single data point.
As our software engineer, you’ll work with the engineering lead to build the orchestration systems that make all of this possible. You’ll scale the scheduling, workflow, and data provenance systems that coordinate furnaces, dispensers, diffractometers, and more — turning scientific intent into fully attributable, reproducible outcomes at scale.
This is a full-stack, production-grade role. You’ll work across Python backends, React interfaces, and cloud infrastructure to ship systems that run reliably with minimal intervention. You’ll work closely with our engineering lead and directly alongside scientists in the lab — understanding where things break, and building the systems that make them not break.
What You’ll DoOwn and evolve the platform that orchestrates our automated synthesis lab — scheduling instruments, managing workflows, and tracking samples end-to-end
Build workflow orchestration for multi-step synthesis pipelines, including DAG execution, dependency resolution, and retry logic for long-running lab processes
Design and implement instrument scheduling systems that handle contention, prioritization, and batching across shared equipment with competing demands
Ensure complete data provenance — every sample, every action, every result is fully traceable with unambiguous lineage
Build the React interfaces that give scientists and lab operators visibility into experiment state, queue status, and system health
Work closely with infrastructure and lab engineering to keep systems reliable as we scale instrument count and experiment throughput
Identify bottlenecks in how science gets done and turn them into software before they become crises
Building production MES, LIMS, ERP, or process control systems where correctness is non-negotiable
Workflow and DAG orchestration — designing execution graphs that are robust, inspectable, and recoverable
Concurrent systems: resource locking, scheduling, and contention handling across shared infrastructure
Data modeling for audit and provenance use cases, including event sourcing or append-only architectures
Full-stack development across Python, React, and cloud-native infrastructure (Kubernetes a plus)
Scheduling algorithms for shared resources with hard and soft constraints
Working in or alongside physical lab, manufacturing, or materials environments
Direct experience in powder synthesis, ceramics, or materials manufacturing environments
Familiarity with laboratory instrumentation protocols and instrument communication standards
Experience with event sourcing or CQRS architectures at production scale
What We Do
We're building AI scientists and the autonomous laboratories for them to operate.
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