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Join Jump Trading as a Campus AI Research Engineer to apply cutting-edge machine learning techniques in financial markets. Collaborate with researchers and quants to optimize ML models and frameworks.
Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.
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.
Skills You'll Need:
- 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 TensorFlow.
- 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.
Bonus Points:
- 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.