Senior GPU HPC Engineer

Hudson River Trading; Greater London; On-site; GBP 113,000 - 225,000; Full time

Job summary

Hudson River Trading (HRT) is seeking a skilled GPU Systems Engineer to scale and evolve an exceptionally sophisticated HPC/AI research environment. You will join a global research and development team, helping design and operate large-scale GPU clusters, with automation, performance tuning, and cross-team collaboration.

Responsibilities include optimizing GPU workloads, deploying hardware/software, and driving infrastructure projects end-to-end.

Qualifications

• 5+ years in large-scale Linux systems engineering (HPC/AI).
• Expertise in Linux installation, performance tuning, and troubleshooting.
• Experience with GPU workloads and optimization.
• Proficiency in Python scripting and automation.
• CUDA or C/C++ experience is a plus.
• Experience with NVIDIA technologies beyond CUDA (NCCL, GPUDirect RDMA, NVLink).
• Familiarity with configuration management tools (Salt, Ansible, Puppet, Chef).

Responsibilities

• Design, build, and optimize large-scale distributed GPU compute clusters.
• Identify and resolve GPU workloads' performance bottlenecks across compute, storage, and networking layers.
• Collaborate with research and development teams to profile, benchmark, and fine-tune GPU-based workloads.
• Automate system deployment, monitoring, and troubleshooting across thousands of nodes.
• Collaborate with research and engineering teams to support evolving workloads.
• Own critical infrastructure projects - from concept to implementation and support.
• Test and deploy new hardware and software, and partner with vendors to resolve complex issues.

Skills

Linux systems

HPC/AI experience

Python scripting

GPU optimization

Distributed workloads

Tools

Salt

Ansible

Puppet

Chef

NVLink

GPUDirect RDMA

NCCL

CUDA

Job description

Hudson River Trading (HRT) is seeking a skilled GPU Systems Engineer to scale and evolve an exceptionally sophisticated HPC/AI research environment. You will join a global research and development team, helping design and operate large-scale GPU clusters, with automation, performance tuning, and cross-team collaboration.

Responsibilities include optimizing GPU workloads, deploying hardware/software, and driving infrastructure projects end-to-end.