GPU Systems Engineer in Mundelein

As part of R&D, you will join the engineers responsible for the compute, storage, operating systems, and automation behind that work at serious scale: hundreds of petabytes of storage and large CPU and GPU clusters spanning thousands of nodes.

• 5+ years engineering large-scale Linux systems in HPC, AI, or distributed-infrastructure environments. \n

• Deep Linux fundamentals: installation, performance tuning, and debugging, down to the kernel when the problem calls for it. \n

• Hands-on troubleshooting of distributed GPU workloads, with a strong mental model of GPU performance. \n

• Working experience with GPUDirect RDMA. You understand how data moves between GPUs and the network, and what to check when it does not. \n

• Solid Python for automation and tooling, plus CUDA or C/C++ experience. You can read, profile, and debug GPU code, not just operate the clusters it runs on. \n

• Familiarity with configuration management tools such as Salt, Ansible, Puppet, or Chef.

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• Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology through to production operation. \n

• Track down performance bottlenecks across the full stack: compute, storage, network, and the seams between them. \n

• Partner with researchers to profile and benchmark GPU workloads, then turn the findings into measurable speedups. \n

• Build the automation that lets a small team operate thousands of nodes: provisioning, monitoring, diagnostics, and self-healing. \n

• Own infrastructure projects end to end, from scope and design through implementation and long-term support. \n

• Qualify new of hardware and software, and work directly with vendors to root-cause complex issues. \n

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