Machine Learning Engineer Internship
A mentored internship on Jane Street's ML teams, working on real engineering projects with access to a large GPU cluster. Interviews follow the software engineering intern route plus an ML engineering round.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Build or improve a piece of ML tooling used by researchers
- Train and evaluate a model on noisy financial data
- Optimise part of a training or inference workflow
- Compare textbook ML methods with what works on market data
What they ask for
Required
- Undergraduate, postgraduate or PhD student
- Hands-on experience training an ML model, working on an ML library or optimising an ML workflow
- Strong programming
- Curiosity and willingness to ask questions
- English fluency
- Education
- Current undergraduate, master's or PhD student.
- Experience
- Students with practical ML project experience
How to prepare
- Train a small model end to end in PyTorch and profile where time goes
- Practise coding interview problems under time limits
- Read about why financial data is noisy and non-stationary
- Contribute to an open-source ML library or build a reproducible training pipeline
What to study
- Python and Data Primer
- Financial Machine Learning
- Probability, Statistics and Financial Data
- Quantitative Data Engineering
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Jane Street’s site before applying: requirements, location and pay can change after we check them.
View the posting on Jane Street’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Jane Street and doesn’t handle applications.