About the opportunity
The Machine Learning Engineer internship sits between software engineering and applied machine learning. You are paired with full-time engineers and work on ML projects that Jane Street actually needs, rather than a separate intern-only codebase.
What you will do
Projects may involve building new ML infrastructure, improving training or deployment workflows, or investigating engineering questions around large-scale machine-learning systems. Interns share parts of the Software Engineering curriculum, then receive additional training focused specifically on ML applications and techniques.
Jane Street describes machine learning as a core part of its global trading business. The trading environment provides unusually fast feedback on models, while also forcing engineers to deal with noisy financial data rather than clean benchmark datasets. Interns gain access to a large GPU cluster containing thousands of modern accelerators, including H100, H200, and B200-class hardware.
Interview and candidate profile
The interview process follows the Software Engineering internship structure, but adds an ML-engineering assessment. After an initial coding interview, candidates who progress to the on-site stage face several technical rounds, including one focused specifically on ML engineering.
Jane Street is looking for undergraduate, postgraduate, or PhD students who have actually trained a model, worked on an ML library, or optimised an ML workflow. Strong programming ability matters heavily, as do curiosity, collaboration, and the ability to learn unfamiliar technology quickly.
