ML Systems Engineer

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About this role

ML Systems Engineer Menlo Park, California, United States About Periodic Labs The most important scientific discoveries of our time won't happen in a traditional lab. 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 You will own the systems layer that makes our frontier model training and inference fast, efficient, and tightly coupled to the RL feedback loop that drives scientific discovery. This is not a pure infrastructure role and it is not a pure research role - it sits exactly at their intersection. You will go deep into the stack: scheduling, kernels, RDMA, weight synchronization, and communication primitives, while working shoulder-to-shoulder with researchers to co-design the algorithms and infrastructure together. The RL loop is central to how Periodic Labs works. Models propose experiments, experiments generate data, data feeds back into training. The speed and reliability of that loop is a direct multiplier on the pace of scientific discovery. You will own the infrastructure that makes it fast. What You'll Do - Build rack and topology-aware scheduling for GB series GPUs across Ray, Slurm, and Kubernetes, minimizing latency and maximizing utilization across heterogeneous cluster configurations - Build online and offline profilers that surface bottlenecks across the training and inference stack and translate findings into

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