GPU Systems Engineer

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.

The role is broad by design. One week you might be shaping the architecture of a new AI cluster, the next profiling a training job that will not scale, the next writing automation that keeps the whole fleet healthy with minimal human intervention.

Responsibilities:

• Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology through to production operation.
• Track down performance bottlenecks across the full stack: compute, storage, network, and the seams between them.
• Partner with researchers to profile and benchmark GPU workloads, then turn the findings into measurable speedups.
• Build the automation that lets a small team operate thousands of nodes: provisioning, monitoring, diagnostics, and self-healing.
• Own infrastructure projects end to end, from scope and design through implementation and long-term support.
• Qualify new generations of hardware and software, and work directly with vendors to root-cause complex issues.

Qualifications:

• 5+ years engineering large-scale Linux systems in HPC, AI, or distributed-infrastructure environments.
• Deep Linux fundamentals: installation, performance tuning, and debugging, down to the kernel when the problem calls for it.
• Hands-on troubleshooting of distributed GPU workloads, with a strong mental model of GPU performance.
• Working experience with GPUDirect RDMA. You understand how data moves between GPUs and the network, and what to check when it does not.
• 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.
• Familiarity with configuration management tools such as Salt, Ansible, Puppet, or Chef.
• Comfort diagnosing problems that cross hardware, OS, and network boundaries rather than stopping at one layer.
• Clear communication. You will work daily with researchers, engineers, and vendors.

Comp: 200-300K + Bonus