Senior Manager, AI and HPC Cluster Engineering Enablement

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is seeking a senior engineering manager in Applied Systems Engineering to help internal engineers adopt next-generation AI/HPC clusters as they move from initial setup to broader use. The role leads cluster engineering support by building an engineering community, creating hands-on training, and managing requests from engineering users and supporting teams. It establishes clear processes for request intake, prioritization, ownership, and communication, covering onboarding, workload assistance, and access to specialists for in-depth debugging. The manager partners across architecture, bringup, software, firmware, networking, storage, datacenter operations, performance, and program teams. The role applies large-scale infrastructure knowledge to identify needs, coordinate resources, and keep requests moving. It also uses support metrics and user feedback to improve service quality, inform priorities and capacity needs, and turn recurring issues and specialist findings into reusable guidance and workflow improvements.

Requirements

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 of technical leadership or engineering management experience, including mentoring engineers, setting priorities, and delivering across organizational boundaries.
Practical understanding of Linux clusters, GPU workload scheduling, cluster management, networking, servers, and large-scale shared storage, with the ability to understand technical requests and engage the appropriate specialists.
Experience coordinating engineering requests, support queues, or operational workflows, including prioritization, ownership, handling blocking issues, and communication through completion.
Ability to translate complex infrastructure concepts into clear documentation, training, and guidance for engineers with different experience levels.
Strong collaboration and communication skills, with sound judgment in balancing user needs, engineering priorities, and resource constraints in an evolving hardware and software environment.

Nice To Haves

Experience designing or operating large HPC/AI clusters and supporting dependent engineers, especially during early hardware, firmware, or software lifecycle phases.
Experience with cluster management platforms and workload schedulers, GPU resource allocation, and large or petabyte-scale filesystems.
Success forming engineering communities, induction programs, technical workshops, or peer-learning activities that increase user self-sufficiency and performance.
Experience improving request workflows across compute, networking, storage, and software teams with clear handoffs, shared priorities, and measurable service improvements.
Experience developing inclusive teams that foster ownership, knowledge transfer, thoughtful decision-making, and career growth.

Responsibilities

Lead and grow a team responsible for cluster engineering support, community involvement, training, and managing engineering requests.
Build an internal engineering community through onboarding, office hours, technical workshops, and knowledge sharing.
Create practical training and documentation for cluster access, workload submission, GPU resource scheduling, shared storage, operational procedures, and actionable support requests.
Own request intake and prioritization by clarifying needs, capturing impact and priorities, identifying dependencies, and maintaining a transparent queue aligned with engineering goals and capacity.
Coordinate specialist investigations and deep-debug support; connect requesters with technical owners, agree on next steps, track progress, and escalate stalled requests.
Partner with cluster operations and engineering teams on access, allocations, maintenance communications, and resource needs.
Convert 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.