Compute Engineering Lead

Compute Engineering Team Lead

Location: Chicago, or New York City, onsite

An elite global Hedge Fund is seeking a hands-on Compute Engineering Team Lead to lead and actively contribute to the design, automation, and operation of a large-scale compute platform. As a hands on leader you will spend a significant portion of you time architecting solutions, writing code, troubleshooting infrastructure, and partnering with engineers to solve complex technical challenges.

Responsibilities

• Lead and mentor a team of compute and infrastructure engineers while remaining deeply involved in day-to-day technical work.
• Design, build, and operate large-scale bare-metal Kubernetes and Linux platforms.
• Drive automation, tooling, and platform improvements using Python and Infrastructure-as-Code practices.
• Partner with developers, researchers, and business stakeholders to deliver scalable and reliable compute services.
• Collaborate with networking, storage, and platform teams to optimize performance, reliability, and efficiency.
• Establish engineering standards, operational best practices, and long-term platform strategy.

Requirements

• 8+ years of infrastructure, platform, Linux, or systems engineering experience.
• Prior experience leading engineers as a manager, tech lead, or team lead.
• Extensive hands-on experience operating bare-metal Kubernetes environments at scale.
• Deep expertise with Linux systems engineering and performance troubleshooting.
• Strong Python skills for automation and platform tooling.
• Experience supporting large compute environments consisting of thousands of servers.
• Excellent problem-solving and communication skills.

Preferred Experience

• Trading firm, hedge fund, quantitative research, or other performance-sensitive environments.
• High Performance Computing (HPC), distributed computing, or large-scale research platforms.
• Networking knowledge including TCP/IP, routing, switching, DNS, and low-latency infrastructure.
• Experience with Slurm or similar workload schedulers.
• Exposure to GPU, AI/ML, or large-scale research compute environments.