Campus AI Research Engineer – Deep Learning(Full-Time)

Jump Trading•Chicago, IL

About The Position

Jump Trading Group is committed to world-class research, empowering 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, fostering constant innovation through fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking individual talent by incentivizing 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. Our trading teams are dynamic groups of traders, quantitative researchers, and engineers who collaborate to examine global markets, understand complex traded products and exchanges, and leverage statistical analysis and data mining skills to develop profitable predictive trading models. We are seeking research scientists with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains. The ideal candidate will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including AI/ML expertise, engineering pragmatism, statistics, and market intuition.

Requirements

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

Nice To Haves

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

Responsibilities

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.