Campus AI Research Engineer - Deep Learning; Intern

What You’ll Do

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

• Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open‑source AI research.
• Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g., transformers, SSMs).
• Solid development skills in Python and/or C++.
• Familiarity with ML libraries/frameworks such as PyTorch, JAX, and/or Tensor Flow.
• Intellectual curiosity, versatility, and originality combined with a pragmatic outlook.
• Ability to thrive in a collaborative, team‑oriented environment.
• Ability to reason through quantitative problems and communicate effectively with trading researchers.
• Reliable and predictable availability.

• Experience with HPC and distributed large‑model training.
• Experience with GPU performance optimization (CUDA or ROCm).
• Experience with end‑to‑end model development.
• Strong opinions on best practices in ML research, tooling, and/or infrastructure.

INTERNATIONAL STUDENTS are encouraged to apply. We accept students eligible for CPT/OPT and we sponsor work visas for full‑time positions.

The estimated base salary for this role (annualized) is $300,000 per year.