Senior HPC Cluster Engineer - AI, ML

NVIDIA Gruppe; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

NVIDIA seeks a Senior AI/ML HPC Cluster Engineer to lead and scale AI/HPC compute infrastructure. You will manage production GPU clusters, coordinate upgrades, and drive reliability and efficiency across on-prem and cloud environments.

You will work with global teams to deliver a top-tier user experience for researchers and engineers. The ideal candidate has 5+ years building large-scale compute systems, experience with Slurm/K8s/PBS, Linux, and containerization.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering or related field or equivalent experience.
• 5+ years of experience designing and operating large scale compute infrastructure.
• Experience with AI/HPC advanced job schedulers (Slurm, K8s, PBS, RTDA, BCM, LSF).
• Proficient in CentOS/RHEL and/or Ubuntu Linux distributions.
• Solid understanding of cluster configuration management tools (BCM, Terraform, Ansible, Puppet, Salt), container technologies (Docker, Singularity, Podman, Shifter, Charliecloud), Python, and Bash scripting.
• Applied experience with AI/HPC workflows that use MPI.
• Experience analyzing and tuning performance for AI/HPC workloads.
• Passion for continual learning and staying ahead of emerging HPC/AI infrastructure technologies.

Responsibilities

• Provide leadership in systems administration and service delivery on our AI/HPC fleet, coordinating upgrades, incidents, and reliability improvements.
• Collaborate with global teams to deliver a world-class user experience in AI and HPC research.
• Own day-to-day operations of production AI/HPC clusters, ensuring health, satisfaction, and resource efficiency.
• Develop and improve our GPU-accelerated computing ecosystem, including scalable automation.
• Build and maintain heterogeneous AI/ML clusters on-premises and in the cloud.
• Foster customer and cross-team relationships to meet evolving user needs.
• Support researchers with workload runs, performance analysis, and optimizations.
• Analyze and optimize cluster efficiency and GPU waste to meet SLAs.
• Lead RCA and postmortems for reliability incidents.
• Participate in on-call rotation and incident response for critical GPUs clusters.

Skills

System Admin

HPC scheduling

Slurm

K8s

RHEL/CentOS

Terraform

Ansible

Python

MPI

Performance tuning

Education

Bachelor's degree in Computer Science / Electrical Engineering

Tools

Docker

Singularity

Podman

Shifter

Charliecloud

Job description

NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Superclusters (MARS) builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you'll help design solutions that power some of the world’s most advanced computing workloads.

NVIDIA is looking for a Senior AI/ML HPC Cluster Engineer to join our MARS team. You will provide leadership and strategic guidance on the management of large-scale HPC systems including the deployment of compute, networking, and storage. You will be working with a team of passionate and skilled engineers across NVIDIA that are continuously working to provide better tools to build and manage this infrastructure. Ideal candidate is strong in building and maintaining distributed clusters, driving improvements, and has the ability to understand researcher computing needs.

What you'll be doing:

• Provide leadership in systems administration and service delivery on our AI/HPC fleet by coordinating system upgrades, responding to incidents, and delivering reliability improvements.
• Collaborate closely with global teams to deliver a world class user experience in AI and HPC research.
• Own day-to-day operations of production AI/HPC clusters, ensuring system health, user satisfaction, and efficient resource utilization.
• Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutions.
• Build and maintain heterogeneous AI/ML clusters on-premises and in the cloud.
• Create and cultivate customer and cross-team relationships to meet user evolving user needs.
• Support our researchers to run their workloads including performance analysis and optimizations.
• Analyze and optimize cluster efficiency, job fragmentation, and GPU waste to meet internal SLA targets.
• Conduct root cause analysis and suggest corrective action Proactively find and fix issues before they occur.
• Lead SEV triage and postmortems for reliability incidents affecting users or infrastructure.
• Participate in on-call rotation and incident response for critical production GPU clusters.

What we need to see:

• Bachelor's degree in Computer Science, Electrical Engineering or related field or equivalent experience.
• Minimum 5 years of experience designing and operating large scale compute infrastructure.
• Experience with AI/HPC advanced job schedulers, such as Slurm, K8s, PBS, RTDA, BCM, or LSF.
• Proficient in administering Centos/RHEL and/or Ubuntu Linux distributions.
• Solid understanding of cluster configuration management tools (BCM, Terraform, Ansible, Puppet, Salt, etc.), container technologies (Docker, Singularity, Podman, Shifter, Charliecloud), Python programming, and bash scripting.
• Applied experience with AI/HPC workflows that use MPI.
• Experience analyzing and tuning performance for a variety of AI/HPC workloads.
• Passion for continual learning and staying ahead of emerging technologies and effective approaches in the HPC and AI/ML infrastructure fields.

Ways to stand out from the crowd:

• Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking.
• Experience with AI/ML concepts, algorithms, models, and frameworks (PyTorch, Tensorflow).
• Experience with InfiniBand with IPoIB and RDMA.
• Understanding of fast, distributed storage systems such as Lustre and GPFS for AI/HPC workloads.