System Software Engineer – Data Center GPU Compute Diagnostics

NVIDIA•Durham, NC

5d•$152,000 - $241,500

About The Position

We are seeking a system software engineer to work on next-generation Data Center GPU diagnostics for rack-scale AI supercomputer systems. Our charter is to build applications and compute workloads that test and heavily stress GPU compute engines, HBM memory, cache hierarchy, PCIe/NVLink interfaces, power delivery, and thermal behavior, and to use those applications in silicon/system bring-up along with packaging such tools for manufacturing and customer use. In this role you will partner with a senior engineer leading the team's CUDA kernel and GEMM diagnostics work, owning well-scoped pieces of the codebase end-to-end while ramping on GPU microarchitecture and silicon characterization. The best candidates will have experience writing low-level diagnostic, performance, or stress software for complex hardware systems, ideally including experience with GPUs, CUDA kernels, GEMM-style workloads, CPUs, NICs or high-speed interconnects such as PCIe. Good interpersonal skills are required as this role will involve close collaboration with hardware architecture, silicon validation, manufacturing and field teams. In addition, the engineer will grow their knowledge of operating systems, computer architecture, GPU memory, voltage/frequency behavior, thermal limits, high-speed buses, and modern AI development and analysis tools to efficiently validate and test next-generation processors and systems. Join an exciting, rewarding and fast paced environment!

Requirements

BS or MS degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
5+ years of system software, GPU software, embedded software, or hardware validation experience.
Experience writing low-level diagnostics, interacting with device firmware and hardware level debuggers.
Strong C/C++ and Python programming skills.
Exposure to GPU architecture, CUDA kernels, GPU compute workloads, or related accelerator programming is strongly preferred.
Working knowledge of memory systems, ECC behavior and DMA engines.
Familiarity with GEMM-style workloads.
Awareness of voltage/frequency characterization, thermal testing, power stress, or related silicon validation concepts such as Vmin/Fmax and P-state testing.
Experience using modern AI development and analysis tools to improve engineering velocity, including code development, debugging, and test creation.
Strong problem solving and low-level debugging skills.

Responsibilities

Working closely with hardware architecture, driver, manufacturing, and field teams through the product development lifecycle of rack-scale AI systems.
Implementing and maintaining CUDA/C++ diagnostic workloads and software infrastructure used in chip development, validation, productization, and field triage.
Writing and tuning GPU compute tests that stress Tensor Cores, SMs, L2/cache hierarchy, HBM memory, and related power/thermal operating points.
Implementing and tuning GEMM-style diagnostic workloads, including tests combined with additional load in NVLink, PCIe or CPU subsystems.
Contributing to higher-level AI workload tests, including PyTorch-based large model workloads that stress GPUs, memory, interconnects, thermals, and system software under realistic rack-scale AI use cases.
Bringing up and validating new hardware features with pre-beta GPU drivers, low-level diagnostic software, and system telemetry, under guidance from the technical lead.
Triaging and debugging failures involving ECC, HBM behavior, thermal limits, voltage/frequency margining, and PCIe/NVLink errors.