NVIDIA's Deep Learning Frameworks (DLFW) Infrastructure team is looking for a deeply technical Senior HPC Cluster Administrator to lead the design, deployment, and reliability of our large-scale GPU compute clusters. These systems run the most demanding deep learning training, inference, and high-performance computing workloads in the industry — from DGX/HGX platforms to ground-breaking Grace Blackwell systems. You will drive architectural decisions across compute, networking, and storage, and partner closely with software, research, and product teams to keep our infrastructure ahead of the workloads it supports.
What you'll be doing:Own the full lifecycle of GPU compute clusters — procurement, provisioning, configuration management, monitoring, and deprecation — across heterogeneous Linux environments (DGX, HGX, embedded systems)
Design and scale storage solutions (NFS, Lustre, WekaFS, or equivalent) with a clear roadmap for capacity and performance growth
Lead automation of infrastructure using modern IaC tools (Ansible, Terraform) and CI/CD pipelines (GitLab)
Manage and optimize job scheduling via Slurm, including fair-share policies, reservation management, and MIG/GPU partitioning strategies
Maintain and improve observability stacks (Prometheus, Grafana, DCGM) and drive proactive resolution of hardware and software incidents
Collaborate with ML engineers and software teams to tune cluster configuration for large-scale distributed training workloads
Evaluate and introduce new technologies — networking fabrics (InfiniBand, NVLink, EFA/RDMA), storage tiers, container runtimes — to improve performance and reliability
Mentor junior engineers and contribute to team-wide engineering standards
BS/MS in CS, EE, CE, or equivalent hands-on experience
5+ years of experience deploying and administering large-scale HPC or ML training clusters
Deep expertise in Linux systems administration at scale
Strong scripting and automation skills in Python and/or bash
Hands-on experience with Slurm (scheduling, accounting, cgroup configuration)
Proficiency with configuration management and IaC (Ansible required; Terraform a plus)
Experience with container technologies (Docker, Apptainer/Singularity, Kubernetes)
Solid understanding of high-speed networking (InfiniBand, RoCE, RDMA, EFA)
Experience with distributed/parallel filesystems and storage architecture
Ability to own problems end-to-end and communicate clearly with engineering and management stakeholders
Experience with NVIDIA GPU infrastructure tools (DCGM, nvidia-smi, MIG, NVSwitch diagnostics)
Familiarity with cluster management platforms (Colossus, Bright Cluster Manager, xCAT, or similar)
Experience supporting large-scale distributed deep learning workloads (PyTorch, JAX, Megatron)
Knowledge of BMC/IPMI/Redfish for out-of-band management and hardware lifecycle
Background in MLOps tooling or ML platform engineering
Join our team of world-class engineers and be part of the groundbreaking work we do at NVIDIA. We are committed to encouraging a collaborative and inclusive environment, where every team member has the opportunity to thrive and make a significant impact!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 221,250 PLN - 383,500 PLN for Level 3, and 292,500 PLN - 507,000 PLN for Level 4.Skills Required
- BS/MS in Computer Science, Electrical Engineering, Computer Engineering, or equivalent hands-on experience
- 5+ years deploying and administering large-scale HPC or ML training clusters
- Deep expertise in Linux systems administration at scale
- Strong scripting and automation skills in Python and/or bash
- Hands-on experience with Slurm (scheduling, accounting, cgroup configuration, MIG/GPU partitioning)
- Proficiency with configuration management and IaC (Ansible)
- Terraform experience
- Experience with container technologies (Docker, Apptainer/Singularity, Kubernetes)
- Solid understanding of high-speed networking (InfiniBand, RoCE, RDMA, EFA)
- Experience with distributed/parallel filesystems and storage architecture (NFS, Lustre, WekaFS or equivalent)
- Ability to own problems end-to-end and communicate clearly with engineering and management stakeholders
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
