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 As HPC Engineer at Periodic Labs, you will design, build, and operate the high-performance computing infrastructure that powers our AI and scientific research. Our models demand extreme compute at scale — large GPU and CPU clusters, high-speed interconnects, low-latency parallel storage, and workload schedulers that make every cycle count. You will work directly with researchers and infrastructure engineers to ensure our compute environment is fast, reliable, and optimized for scientific discovery at the frontier. This is a deeply hands-on role. You will architect and tune systems, automate provisioning, diagnose performance bottlenecks, and design for resilience at scale. You’ll partner with research and ML teams to understand their workloads and shape an HPC environment that removes friction and accelerates science. What You’ll Do Design, deploy, and operate large-scale GPU and CPU clusters for AI training, scientific simulation, and research workloads Manage and optimize high-speed interconnect fabrics (InfiniBand, RoCE) and parallel filesystems (Lustre, GPFS, WEKA, or equivalent) for maximum throughput and minimum latency Own workload scheduling and resource management using Slurm, Kubernetes, or similar systems — tuning for throughput, fairness, and researcher productivity Implement and maintain automated cluster provisioning, configuration management, and lifecycle tooling using Ansible, Terraform, or custom orchestration Monitor cluster health, performance, and utilization; build dashboards and alerting to proactively identify and resolve bottlenecks Partner with research and ML engineering teams to profile workloads, diagnose performance issues, and tune hardware and software stacks for specific computational demands Design and implement backup, disaster recovery, and fault-tolerance strategies for research data and compute infrastructure Evaluate and integrate new hardware (GPUs, accelerators, networking) and software technologies as the field evolves Establish standards and runbooks for HPC operations, capacity planning, and incident response Collaborate with security and infrastructure teams to implement access controls, network segmentation, and compliance controls appropriate for a research environment You Will Thrive in This Role If You Have Experience designing and operating large-scale HPC or GPU clusters in research, cloud, or enterprise environments Deep knowledge of high-speed interconnects such as InfiniBand (HDR/NDR) or RoCE, including fabric management, tuning, and troubleshooting Hands-on experience with parallel and distributed storage systems (Lustre, GPFS, WEKA, BeeGFS, or similar) — configuration, performance tuning, and capacity management Experience with workload managers and schedulers such as Slurm, PBS Pro, LSF, or Kubernetes-based HPC orchestration Linux systems administration at scale, including kernel tuning, NUMA optimization, CPU and memory affinity, and GPU driver management Infrastructure automation using Ansible, Terraform, or equivalent — you treat infrastructure as code Experience with GPU computing environments including CUDA, NCCL, MPI, and multi-node distributed training or simulation setups Performance profiling, benchmarking, and tuning of computational workloads across CPU, GPU, memory, network, and storage Experience with monitoring and observability tooling (Prometheus, Grafana, or equivalent) in large, heterogeneous compute environments Ability to collaborate with researchers or data scientists to understand workload