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About the Role

This role offers a unique educational experience, working alongside experienced Machine Learning Researchers on projects relevant to systematic trading strategies. You will learn about market dynamics through challenging classes and activities, applying established and novel methods to train practical models. The position provides access to vast datasets and significant computing resources, including a large GPU cluster, to tackle complex trading challenges.

Responsibilities

Work closely with full-time machine learning researchers on projects.
Conduct end-to-end studies of unexplored datasets.
Explore new modeling paradigms for complex problems.
Consider novel approaches to unsolved problems.
Dive deep into market data.
Tune hyperparameters.
Debug training issues.
Analyze model predictions.

Requirements

Undergraduate, PhD student, or postdoc with practical experience in ML problems.
Interest in applying logical and mathematical thinking to problems.
Curiosity about the machine learning landscape.
Excitement to apply state-of-the-art techniques.
Ability to rapidly implement and iterate on ideas in Python and a favorite ML framework.
Eagerness to ask questions, admit mistakes, and learn new things.
Fluent in English.

Skills

Machine Learning
Python
ML framework implementation
Data analysis
Model training
Hyperparameter tuning
Debugging

Experience Level

Undergraduate, PhD student, or postdoc

Education Level

Undergraduate
PhD
Postdoc

About the Company

Jane Street is a quantitative trading firm where the lines between research, technology, and trading are intentionally blurred.
The firm has access to petabytes of data and a large-scale computing infrastructure, including a significant GPU cluster.
Trading presents unique challenges such as large models, nonstationary datasets, and competitive multi-agent environments, driving the search for novel techniques.