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Aquatic Capital Management

Systems Engineer

ChicagomidAdded 1 month ago

About this role

Aquatic Capital Management seeks an experienced Systems Engineer to architect and manage compute infrastructure supporting quantitative research and trading operations. You'll own on-premise and hybrid cloud systems including Slurm/Ray clusters, high-performance storage, and GPU workloads while mentoring junior engineers and driving infrastructure improvements.

What you'll do

  • Own and evolve Slurm and Ray cluster infrastructure across on-premise and GCP environments
  • Manage high-performance storage systems (VAST) and resolve performance bottlenecks across storage, network, and compute
  • Design provisioning systems for compute resources and maintain CI/CD infrastructure (GitHub Actions)
  • Architect infrastructure decisions, provide technical direction, and mentor earlier-career engineers
  • Diagnose and resolve complex system issues across the full computing footprint
  • Evaluate and implement new compute paradigms as research needs evolve

What they're looking for

  • Unix/Linux systems administration across multiple distributions
  • HPC grid systems (SLURM expertise required)
  • High-performance/parallel storage systems (VAST, GPFS, Lustre)
  • Scripting and automation (Bash, Python)
  • Bare-metal infrastructure and datacenter provisioning
  • Distributed compute frameworks (Ray, Dask, Spark preferred)
  • GPU computation and performance optimization
  • Cloud infrastructure (GCP and AWS experience)
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Aquatic Capital Management

Aquatic Capital Management is a quantitative investment firm that builds high-performance distributed systems and infrastructure for financial markets research and trading operations. The company is hiring systems engineers, software engineers, and interns to develop low-latency platforms, manage compute infrastructure including GPU clusters, and deploy quantitative models to live trading systems.

View all jobs at Aquatic Capital Management

Likely interview questions

  • Walk us through your experience managing SLURM or similar HPC grid systems in production. How have you optimized scheduling efficiency and throughput for research workloads?
  • Describe a time you diagnosed and resolved a complex performance bottleneck spanning storage, network, and compute layers. What was your approach?
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