Campus AI Research Engineer (intern)

Jump Trading Jump Trading · Quant · Chicago, New York City · Front Office

Seeking world-class engineers to collaborate with research, trading, and engineering teams to build state-of-the-art ML systems for quantitative finance. Responsibilities include optimizing training pipelines on HPC, developing low-latency inference systems, and integrating AI/ML models into production. Requires expertise in Python/C++, ML frameworks, and GPU programming, with experience in large-scale AI/ML systems.

What you'd actually do

  1. Apply state-of-the-art techniques to complex and challenging domains.
  2. Work closely with researchers and quants to build flexible and reusable frameworks for financial AI/ML.
  3. Optimize training pipelines to make the best use of our HPC resources.
  4. Integrate AI/ML models into production systems where latency matters.
  5. Work across a mix of programming languages: C / C++ / Python / CUDA and other low-level GPU languages.
  6. Build large-scale AI/ML systems that are observable, performant, and flexible. Help improve productivity by reducing the iteration cycle time on research.

Skills

Required

  • Proficiency in Python and/or C++
  • Proficiency in PyTorch, JAX, TensorFlow, and/or similar frameworks
  • Expertise in GPU or accelerator programming (CUDA, Triton, SYCL, ROCm, or equivalent)
  • Experience building AI/ML systems at scale (hundreds of TBs of training data, low-latency or high-throughput inference requirements)

Nice to have

  • Creative thinkers who are driven, self-motivated, and eager to solve challenging problems
  • Ability to thrive in a collaborative, team-oriented environment
  • Excellent written and verbal communication skills in English
  • Reliable and predictable availability

What the JD emphasized

  • low-latency inference systems
  • large-scale AI/ML systems
  • low-latency or high-throughput inference requirements

Other signals

  • building state-of-the-art ML systems
  • optimizing training pipelines
  • developing low-latency inference systems
  • advancing AI/ML capabilities