Senior Manager, AI and HPC Cluster Engineering Enablement

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is reshaping accelerated computing and AI infrastructure with advancements in GPUs, networking, software, and large-scale datacenter platforms. We seek a senior engineering manager to support our internal engineers in adopting next-generation AI/HPC clusters as they transition from initial setup to wider usage. Within Applied Systems Engineering, you will be responsible for cluster engineering support by growing the engineering community, creating hands-on training, and handling requests from engineering users and supporting teams. You will set up clear processes for intake, prioritization, ownership, and communication for requests. These will include onboarding, workload help, and access to experts for in-depth debugging. Your knowledge of large-scale infrastructure will enable you to identify needs, coordinate resources, and ensure progress on requests. You will partner with architecture, bringup, software, firmware, networking, storage, datacenter operations, performance, and program teams. The ideal candidate brings engineering leadership and operational experience in Linux-based HPC/AI environments. They excel at developing people, managing competing requests, and converting technical knowledge into effective training and support. What you'll be doing: • Lead and grow a team dedicated to cluster engineering support, community involvement, training, and managing engineering requests. • Develop a connected internal engineering community through onboarding, office hours, technical workshops, and knowledge sharing that help users work effectively on evolving AI/HPC platforms. • Create practical training and documentation for cluster access, workload submission, GPU resource scheduling, shared storage, operational procedures, and submitting actionable support requests. • Own the request intake and prioritization process: clarify the need, capture impact and priorities, identify dependencies, and maintain a transparent queue aligned with engineering achievements and available capacity. • Coordinate requests that require specialist investigation, including deep-debug support. Connect requesters with the appropriate technical owners, agree on next steps, track progress, and bring up stalled requests. • Partner with cluster operations and engineering teams on access, allocations, maintenance communications, and resource needs so users can plan and complete their work. • Turn recurring questions, request patterns, and specialist findings into reusable guidance, training, and proposals for self-service tools or workflow improvements. • Use request volume, response time, aging, completion trends, training participation, and user feedback to improve support quality and communicate priorities and capacity needs. • What we need to see: • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Science, or equivalent practical experience. • 8+ years of experience in high-performance computing, AI infrastructure, datacenter systems, production engineering, or large-scale Linux environments. • 5+ years