GPU Systems Engineer — Large-Scale HPC/AI Infra

Oxford Knight; New York, Chicago (NY, IL); On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Bonuses

Generous benefits package

Job summary

Oxford Knight is seeking a GPU Systems Engineer to lead the design, build, and optimization of large-scale HPC/AI infrastructure across multiple global data centers. You will own end-to-end projects, optimize GPU workloads, and automate operations for thousands of nodes, working with research and engineering teams.

The role emphasizes performance tuning, collaboration across teams, and leveraging NVIDIA technologies beyond CUDA to push the frontier of high-performance computing.

Qualifications

• 5+ years’ Linux systems engineering experience on a large scale in HPC, AI or distributed infrastructure.
• GPU optimization & performance expertise and troubleshooting distributed GPU workloads.
• Strong Python scripting & automation frameworks experience; CUDA or C/C++ is a plus.
• Experience with NVIDIA technologies beyond CUDA (NCCL, GPUDirect RDMA, NVLink).
• Strong communication skills; able to collaborate effectively across diverse technical teams.

Responsibilities

• Design, build and optimize large-scale infrastructure (HPC/AI clusters) from concept to implementation.
• Troubleshoot and automate for thousands of nodes; optimize GPU workloads.
• Collaborate closely with research and engineering teams on infrastructure projects.
• Manage and expand triple-digit petabyte-scale storage and GPU/CPU clusters across data centers.

Skills

Linux systems engineering

GPU optimization

Python scripting & automation

CUDA / C/C++

NVIDIA technologies (NCCL, GPUDirect, 

Cross-team collaboration

Tools

NVIDIA GPU software stack

Job description

Oxford Knight is seeking a GPU Systems Engineer to lead the design, build, and optimization of large-scale HPC/AI infrastructure across multiple global data centers. You will own end-to-end projects, optimize GPU workloads, and automate operations for thousands of nodes, working with research and engineering teams.

The role emphasizes performance tuning, collaboration across teams, and leveraging NVIDIA technologies beyond CUDA to push the frontier of high-performance computing.