GPU Systems Engineer at Hudson River Trading

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

14 Jan, 26

Salary

0.0

Posted On

16 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Linux Systems Engineering, Performance Tuning, Troubleshooting, GPU Optimization, Python Scripting, Automation Frameworks, CUDA, NVIDIA Technologies, Configuration Management Tools, System Diagnosis, Communication Skills, Organizational Skills, Collaboration, Fast-Paced Environments, High-Impact Work

Industry

Financial Services

Description

Hudson River Trading (HRT) is looking for GPU Systems Engineers to help scale and evolve our exceptionally sophisticated HPC/AI research environment. Joining our Research and Development team, you will collaborate with experts responsible for the compute, storage, operating systems, and automation tools that enable our trading and research to run 24/7 across the globe. We design, grow, and operate infrastructure at a large scale, including triple-digit petabyte-scale storage and massive CPU and GPU clusters in globally distributed data centers. As such, this is a high-impact role with broad scope, from HPC/AI cluster design and performance tuning, to troubleshooting and automation for thousands of nodes. 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 Qualifications 5+ years of experience in large-scale Linux systems engineering in HPC, AI or distributed infrastructure roles. Extensive experience in Linux system installation, performance tuning, and troubleshooting Expertise in troubleshooting distributed GPU workloads Deep knowledge around GPU optimization and performance Proficiency in Python scripting and automation frameworks CUDA or C/C++ experience is a plus Experience with NVIDIA technologies beyond CUDA, such as NCCL, GPUDirect RDMA, and NVLink Familiarity with configuration management tools (e.g. Salt, Ansible, Puppet, Chef) Comfortable diagnosing complex system issues at the hardware, OS, and network levels Strong communication and organizational skills; able to collaborate across diverse technical teams Thrive in fast-paced environments and excited by high-impact work Culture Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading. At HRT we welcome a variety of expertise: mathematics and computer science, physics and engineering, media and tech. We’re a community of self-starters who are motivated by the excitement of being at the cutting edge of automation in every part of our organization—from trading, to business operations, to recruiting and beyond. We value openness and transparency, and celebrate great ideas from HRT veterans and new hires alike. At HRT we’re friends and colleagues – whether we are sharing a meal, playing the latest board game, or writing elegant code. We embrace a culture of togetherness that extends far beyond the walls of our office. Feel like you belong at HRT? Our goal is to find the best people and bring them together to do great work in a place where everyone is valued. HRT is proud of our diverse staff; we have offices all over the globe and benefit from our varied and unique perspectives. HRT is an equal opportunity employer; so whoever you are we’d love to get to know you.

Responsibilities

Design, build, and optimize large-scale distributed GPU compute clusters while identifying and resolving performance bottlenecks. Collaborate with teams to automate system deployment and support evolving workloads.