Senior Manager, AI and HPC Cluster Engineering Enablement

NVIDIA•Santa Clara, WA

10d•$272,000 - $488,750

About The Position

NVIDIA is seeking a senior engineering manager within Applied Systems Engineering to support internal engineers adopting next-generation AI/HPC clusters as they move from initial setup to wider usage. The role is responsible for cluster engineering support, growing the engineering community, creating hands-on training, and managing requests from engineering users and support teams. The manager will establish clear processes for request intake, prioritization, ownership, and communication, covering onboarding, workload assistance, and access to experts for in-depth debugging. The role partners across architecture, bringup, software, firmware, networking, storage, datacenter operations, performance, and program teams. It calls for engineering leadership and operational experience in Linux-based HPC/AI environments, along with the ability to develop people, balance competing requests, and translate technical knowledge into effective training and support.

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, managing blocking issues, and communication through completion.
Ability to translate complex infrastructure concepts into clear documentation, training, and guidance for engineers with different levels of experience.
Strong collaboration and communication skills, with sound judgment when 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 improve 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.
Proven experience developing inclusive teams that foster ownership, knowledge transfer, thoughtful decision-making, and career growth.

Responsibilities

Lead and grow a team focused on cluster engineering support, community involvement, training, and engineering requests.
Develop an internal engineering community through onboarding, office hours, technical workshops, and knowledge sharing.
Create practical training and documentation covering cluster access, workload submission, GPU resource scheduling, shared storage, operational procedures, and actionable support requests.
Own request intake and prioritization, including clarifying needs, capturing impact and priorities, identifying dependencies, and maintaining a transparent queue aligned with engineering goals and capacity.
Coordinate requests requiring specialist investigation, connect requesters with technical owners, agree on next steps, track progress, and address 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.