Senior Manager, AI and HPC Cluster Engineering Enablement

NVIDIA; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

NVIDIA Corporation seeks a Senior Engineering Manager to lead cluster engineering support for AI/HPC infrastructure. You will grow the engineering community, create hands-on training, and manage engineering requests with clear intake, prioritization, and ownership.

You will collaborate with architecture, bringup, software, firmware, networking, storage, and datacenter operations to ensure progress, share knowledge, and deliver scalable guidance.

Qualifications

• 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 right 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.

Responsibilities

• 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.

Skills

HPC infrastructure

Engineering leadership

Linux clusters

GPU scheduling

Cluster management

Networking

Shared storage

Documentation & training

Communication

Prioritization

Education

BS/MS/PhD in CS/CE/EE or equivalent

Job description

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 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 right 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
• .

Ways to stand out from the crowd:

• Experience designing or operating large HPC/AI clusters and supporting the engineers who depend on them, especially during early hardware, firmware, or software lifecycle phases.
• Experience with cluster management platforms and workload schedulers, GPU resource allocation, and