More jobs:
Compute Engineering Lead
Job in
Chicago, Cook County, Illinois, 60290, USA
Listed on 2026-07-14
Listing for:
Selby Jennings
Full Time
position Listed on 2026-07-14
Job specializations:
-
IT/Tech
Unix/Linux, SRE/Site Reliability, Systems Engineer
Job Description & How to Apply Below
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 your 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.
- 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.
- 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.
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