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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1664 open roles
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Listed pay typically runs
$184,000–$287,500
across 653 roles with salary data.
The Senior Manager, AI and HPC Cluster Engineering Enablement joins the Applied Systems Engineering team to support internal engineers transitioning to next-generation AI and HPC clusters. This leadership role involves growing an engineering community, creating hands-on training, and managing a request intake process for onboarding, workload help, and deep-debug support. You will develop documentation for GPU resource scheduling, shared storage, and operational procedures while coordinating with architecture, firmware, networking, and storage teams. The role requires 8+ years of experience in high-performance computing, datacenter systems, or large-scale Linux environments, plus 5+ years of technical leadership. You must possess practical knowledge of Linux clusters, GPU workload scheduling, and large-scale shared storage to translate complex infrastructure concepts into reusable guidance and self-service tools for engineers.
Lead and grow a team dedicated to cluster engineering support, community involvement, and training.
Develop an internal engineering community through onboarding, office hours, technical workshops, and knowledge sharing.
Create practical training and documentation for cluster access, workload submission, and GPU resource scheduling.
Own the request intake and prioritization process to clarify needs, identify dependencies, and maintain a transparent queue.
Coordinate specialist investigations and connect requesters with appropriate technical owners for deep-debug support.
Partner with operations and engineering teams on access, allocations, maintenance communications, and resource needs.
Convert recurring questions and request patterns into reusable guidance and proposals for self-service tools.
Use support metrics and user feedback to improve service quality and communicate capacity needs.
What we're looking for
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 and setting priorities.
Practical understanding of Linux clusters, GPU workload scheduling, cluster management, networking, servers, and large-scale shared storage.
Experience coordinating engineering requests, support queues, or operational workflows, including prioritization and managing blocking issues.
Ability to translate complex infrastructure concepts into clear documentation, training, and guidance for engineers.
Experience designing or operating large HPC/AI clusters and supporting engineers during early hardware, firmware, or software lifecycle phases (preferred).
Experience with cluster management platforms, workload schedulers, GPU resource allocation, and petabyte-scale filesystems (preferred).
Remote (Santa Clara, CA)+518 days ago
$184,000–$287,500
CUDA
C++
Python
Deep Learning
GPU Programming
Distributed Training
Inference
Multi-Agent Systems
Reinforcement Learning
Performance Optimization
AI Systems
Foundational Models
Rack-scale AI Infrastructure
Linux
GPU
Networking
Storage
Firmware
BMC
Computer Systems Architecture
NPI
Validation
Debug Workflows
Datacenter Systems
Power
Thermal
Cabling