About the job
We are looking for a dedicated engineer for the Senior Systems Software Engineer role, focusing on GPU Performance at Scale. At NVIDIA, this role is uniquely positioned to drive innovation in AI and GPU computing. You will contribute to world-class computing hardware and software, fueling groundbreaking advancements in artificial intelligence. You will provide insights on large-scale system composition and tuning mechanisms for high-performance compute runs. Collaborate with researchers, developers, and customers to craft improved workflows and develop new, leading solutions. Engage with HPC, OS, CPU, GPU compute, and systems specialists to architect, build, and optimize large-scale performance platforms.
Responsibilities
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 (BMC/SBIOS/OS/drivers, etc.) to develop outstanding methods and tools. Analyze, debug, and resolve critical firmware and software issues to achieve the highest AI workload performance at scale.
Qualifications
Minimum
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 8+ years of applicable experience.
Preferred
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.