Systems Engineer – HPC & Cloud Infrastructure

About the role

Tower Research Capital is seeking a Systems Engineer to design, support, and operate high‑performance computing (HPC) and cloud infrastructure that powers its quantitative trading platform. You will work across on‑premises and public‑cloud environments, ensuring the reliability, scalability, and performance of large‑scale Linux systems.

Key responsibilities

• Design, deploy, and maintain HPC compute and storage clusters in both on‑premises data centers and cloud providers (GCP, AWS, Azure).
• Manage Linux systems covering compute, storage, networking, automation, and monitoring.
• Troubleshoot complex issues across OS, storage, networking, and scheduler layers in collaboration with other infrastructure teams.
• Optimize batch and containerized workloads (CPU and GPU) and improve cluster utilization.
• Develop and maintain HPC management tools, user‑access modules, and internal libraries.
• Create metrics, observability pipelines, and performance analyses to drive efficiency.
• Handle code deployments, upgrades, fixes, and overall infrastructure lifecycle.
• Identify repetitive workflows and automate them to enhance reliability and user experience.

Required profile

• Bachelor’s degree or higher in Computer Science, Engineering, or a related field.
• 1–5 years of experience with Linux systems, DevOps, HPC, or infrastructure engineering.
• Strong understanding of Linux internals such as process scheduling, virtual memory, filesystems, and networking.
• Familiarity with batch schedulers (e.g., Slurm, HTCondor) is a plus.

Required skills

• Linux system administration
• High‑performance computing (HPC) environments
• Cloud platforms: GCP, AWS, Azure
• Batch scheduling (Slurm, HTCondor)
• Containerized workloads (CPU/GPU)
• Automation and scripting
• Monitoring and observability tools
• Code deployment and lifecycle management

What we offer

• Opportunity to work with world‑class trading and engineering talent.
• Access to cutting‑edge hardware, FPGA, and machine‑learning technologies.
• Collaborative, results‑oriented environment that encourages innovation.