About the role
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 actionable optimizations Implement direct S3 checkpoint streaming to eliminate I/O bottlenecks in large-scale training runs Run methodical benchmarking to identify optimal RL training configurations across model sizes, batch strategies, and hardware topologies Write and optimize communication and GPU kernels to extract maximum throughput from the hardware Design and implement zero-copy RDMA weight synchronization between training and inference to keep the RL loop tight and low-latency Build fast sandbox execution environments that allow rapid rollout of model-generated actions and return of rewards without blocking the training pipeline Engage directly with the SGLang, Megatron, and Ray communities — contributing upstream, influencing roadmaps, and pulling in improvements that benefit Periodic Labs’ workloads Work in close collaboration with RL and pretraining researchers to co-design algorithms and infrastructure together — you will shape what is possible at the research level by knowing what is achievable at the systems level, and vice versa The net result: high-throughput, fault-tolerant training and inference systems tightly coupled with a low-latency RL feedback loop that accelerates scientific discovery at every turn. You Might Thrive in This Role if You Have Experience With Large-scale inference infrastructure: load balancing, traffic shifting, scheduling, and serving architecture at production scale Low-level systems programming: RDMA, NVLink, kernel-level work, and network stack optimization GPU cluster scheduling and orchestration across Ray, Slurm, or Kubernetes, with awareness of rack topology and hardware locality Writing and optimizing CUDA kernels, communication primitives, or distributed training collective operations Profiling and benchmarking distributed ML systems to identify and eliminate bottlenecks across compute, memory, and network Checkpoint management and streaming at scale, including direct cloud storage integration Building or contributing to open source ML infrastructure projects (e.g., SGLang, Megatron-LM, vLLM, Ray) Working directly with ML researchers on algorithm-infrastructure co-design — you understand the research well enough to make systems decisions that serve it Mechanics Minimum education: Bachelor’s degree or an equivalent combination of education and training or experience Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role