Senior Systems Software Engineer - GPU Performance at Scale

NVIDIA is a company with over 25 years of experience in computer graphics, PC gaming, and accelerated computing, currently focusing on AI and GPU computing to advance computing hardware and software.

• Proven understanding of accelerated computing software stacks (CUDA).
• Experience with modern cloud and container-based enterprise computing architectures, with Slurm preferred.
• Strong programming and scripting experience in C++, Python, and Bash.
• Deep expertise in systems architecture and understanding of component impact on performance.
• Experience with container technology and Linux-based operating systems, with Docker preferred.
• Experience supporting high-performance computing or deep learning in engineering or academic research communities.
• Strong teamwork and communication skills with results-focused analytical abilities.
• BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience); MS or PhD desirable with 10+ years of applicable experience.

Nice to have:

• End-to-end GPU performance engineering from profiler to systems analysis.
• Linux systems programming and optimization experience.
• Exposure to virtualization techniques and cloud platform solutions.
• Experience with scheduling and resource management systems.
• Experience with large-scale HPC environments.

• Lead the implementation of performance practices in large-scale GPU infrastructure, delivering tools, methodologies, and flows to validate and improve multiple datacenter products concurrently.
• Align next-generation AI workloads with next-generation datacenter builds for NVIDIA GPUs, CPUs, and networking hardware, engaging early with hardware, firmware, software, platform internal and customer teams.
• Develop engineering solutions that provide continuous insights into AI workload performance in evolving environments, generating swift insights into improvements and regressions.
• Decompose high-complexity performance or stability issues into minimal reproduction cases to identify root causes.
• Collaborate with software and firmware teams (BMC, SBIOS, OS, drivers) to develop methods and tools, analyze, debug, and resolve critical firmware and software issues to achieve highest AI workload performance at scale.