GPU Systems Engineer - Low-Latency HPC & AI Clusters

Tower Research Capital; New York (NY); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Generous paid time off policies

Hybrid working opportunities

Free breakfast, lunch & snacks

Wellness reimbursement

Company-sponsored sports teams and gym

Volunteer opportunities

Social events and learning workshops

Job summary

Tower Research Capital is a leading quantitative trading firm that designs and operates high-performance compute infrastructure for systematic trading. This role sits in R&D, focusing on the compute, storage, and automation behind large-scale trading workloads.

You will join engineers responsible for hundreds of petabytes of storage and thousands of GPU-accelerated nodes, shaping architectures for AI clusters and performance profiling. Collaboration with researchers and vendors is daily.

Qualifications

• 5+ years engineering large-scale Linux systems in HPC, AI, or distributed-infrastructure environments.
• Deep Linux fundamentals: installation, performance tuning, and debugging, down to the kernel when the problem calls for it.
• Hands-on troubleshooting of distributed GPU workloads, with a strong mental model of GPU performance.
• Working experience with GPUDirect RDMA. You understand how data moves between GPUs and the network, and what to check when it does not.
• Solid Python for automation and tooling, plus CUDA or C/C++ experience. You can read, profile, and debug GPU code, not just operate the clusters it runs on.
• Familiarity with configuration management tools such as Salt, Ansible, Puppet, or Chef.
• Comfort diagnosing problems that cross hardware, OS, and network boundaries rather than stopping at one layer.
• Clear communication. You will work daily with researchers, engineers, and vendors.

Responsibilities

• Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology through to production operation.
• Track down performance bottlenecks across the full stack: compute, storage, network, and the seams between them.
• Partner with researchers to profile and benchmark GPU workloads, then turn the findings into measurable speedups.
• Build the automation that lets a small team operate thousands of nodes: provisioning, monitoring, diagnostics, and self-healing.
• Own infrastructure projects end to end, from scope and design through implementation and long-term support.
• Qualify new generations of hardware and software, and work directly with vendors to root-cause complex issues.

Skills

Linux systems engineering

Distributed GPU workloads

GPU performance modeling

Python automation

C/C++ development

Tools

CUDA

GPUDirect RDMA

Job description

Tower Research Capital is a leading quantitative trading firm that designs and operates high-performance compute infrastructure for systematic trading. This role sits in R&D, focusing on the compute, storage, and automation behind large-scale trading workloads.

You will join engineers responsible for hundreds of petabytes of storage and thousands of GPU-accelerated nodes, shaping architectures for AI clusters and performance profiling. Collaboration with researchers and vendors is daily.