Senior Staff Engineer - Infrastructure Systems

As Senior Staff Engineer, Infrastructure Systems, you will own the GPU software stack on every Runware node: the firmware, the kernel and NVIDIA driver, the CUDA runtime and libraries, the container runtime, and the way Kubernetes and our inference services see and share a GPU. When a GPU misbehaves anywhere in the fleet, you will find out why and write the software that keeps it from happening again.

This is a senior staff-level individual contributor role for someone who knows GPUs from the driver up and still writes code every day. One month that means qualifying a new driver and CUDA release against every model family we serve. The next it is the GPU part of the acceptance suite that every node must pass, a detector that catches a failing GPU from its Xid and ECC history before a customer does, the bring-up of a new GPU generation, or a CUDA error that turns out to start in firmware, a PCIe link or a kernel update. You will work directly with the node operations, Managed Kubernetes, inference performance and Sonic Inference Engine teams, and be the person each of them calls when the problem sits below the framework.

Success means that every GPU in every pod runs a known, tested combination of firmware, driver and CUDA, that upgrades roll across the fleet without failed inferences, that GPU faults are detected, classified and remediated automatically, and that a new GPU generation goes from delivery to serving traffic in days.

What you'll do

Own the GPU software baseline. Versioned, reproducible combinations of kernel, NVIDIA driver, firmware, CUDA runtime and libraries (cuDNN, cuBLAS, NCCL, TensorRT) and container toolkit, tested before they roll and rolled across the fleet without downtime.

Qualify every driver and CUDA release. Keep the compatibility matrix across GPU generations, frameworks and model families, and catch regressions in correctness, performance and memory use before they reach production.

Take GPU failures to root cause: Xid errors, CUDA "unknown error", cuBLAS handle failures, out-of-memory and fragmentation, GPUs that fall off the bus. Separate hardware, firmware, driver, runtime and application causes, and prove which one it is.

Build GPU acceptance and diagnostics: the burn-in, DCGM diagnostics, memory and interconnect tests and real-workload tests that gate every node and rack before it serves traffic, and the reports customers receive at handover.

Make GPU health visible and self-healing. Pull telemetry from DCGM, the driver, the kernel log and the BMC into one trusted view, classify faults, and automate the drain, reset and replacement paths.

Own how GPUs are exposed to workloads: the container runtime, device plugins and the GPU Operator, MIG and sharing policies, and isolation between tenants, on bare metal and on Kubernetes.

Bring up new GPU generations and new pods with the hardware and node operations teams: first driver and firmware, NVLink and NVSwitch validation, and the automation that makes the next bring-up take days.

Characterize hardware with real workloads. Benchmark image, video, audio and LLM inference on each GPU generation and software stack, and feed the results into placement, pricing and the next purchase.

Work with NVIDIA and upstream projects. File driver and firmware bugs with reproducible cases and see them through, track what each release changes, and contribute fixes to the open-source tools we depend on.

Use AI to accelerate the work: agents for log and Xid triage, test generation, release qualification and runbook automation, so that the team's output grows faster than its headcount.