Senior HPC Cluster Engineer - AI, ML
NVIDIA
NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Superclusters (MARS) builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you’ll help design solutions that power some of the world’s most advanced computing workloads.
NVIDIA is looking for a Senior AI/ML HPC Cluster Engineer to join our MARS team. You will provide leadership and strategic guidance on the management of large-scale HPC systems including the deployment of compute, networking, and storage. You will be working with a team of passionate and skilled engineers across NVIDIA that are continuously working to provide better tools to build and manage this infrastructure. Ideal candidate is strong in building and maintaining distributed clusters, driving improvements, and has the ability to understand researcher computing needs.
What you'll be doing:
- Provide leadership in systems administration and service delivery on our AI/HPC fleet by coordinating system upgrades, responding to incidents, and delivering reliability improvements.
- Collaborate closely with global teams to deliver a world class user experience in AI and HPC research.
- Own day-to-day operations of production AI/HPC clusters, ensuring system health, user satisfaction, and efficient resource utilization.
- Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutions.
- Build and maintain heterogeneous AI/ML clusters on-premises and in the cloud.
- Create and cultivate customer and cross-team relationships to meet user evolving user needs.
- Support our researchers to run their workloads including performance analysis and optimizations
- Analyze and optimize cluster efficiency, job fragmentation, and GPU waste to meet internal SLA targets.
- Conduct root cause analysis and suggest corrective action Proactively find and fix issues before they occur.
- Lead SEV triage and postmortems for reliability incidents affecting users or infrastructure.
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