Senior Systems Software Engineer - GPU Performance at Scale

Requirements:

• 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/C++, Python, Bash.
• Deep expertise in systems architecture and the impact of various components on performance.
• Experience with container technology and Linux-based OSes, with Docker preferred.
• Experience supporting high-performance computing or deep learning in engineering or academic research communities.
• Strong teamwork and communication skills, coupled 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.

What you'll be doing:

• Lead the implementation of performance practices in large-scale GPU infrastructure, delivering powerful 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. Engage early with HW/FW/SW/platform internal and customer teams.
• Develop engineering solutions that provide continuous insights into the performance of AI workloads in evolving environments, generating swift insights into improvements and regressions.
• Decompose high-complexity performance or stability issues into minimal reproduction cases, working towards identifying the root cause.
• Participate in collaborations with various SW and FW teams to develop outstanding methods and tools. Analyze, debug, and resolve critical firmware and software issues to achieve the highest AI workload performance at scale.

Ways to Stand Out From the Crowd:

• End-to-end GPU performance engineering from the 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.