Machine Learning Researcher

About the Program

Our goal is to give you a real sense of what it’s like to work at Jane Street full time as an ML researcher. Over the course of your internship, you will explore ways to approach and solve cutting-edge machine learning problems through fun and challenging classes, interactive sessions, and group discussions — and then you will have the chance to put those lessons to practical use. The Machine Learning Research internship is based in our Hong Kong office, but interns will spend time in both Hong Kong and New York. This opportunity to work globally and gain exposure to the breadth of projects we work on mirrors the full time Machine Learning Researcher experience.

As a Machine Learning Research intern, you are paired with full-time employees who act as mentors, collaborating with you on real-world projects. Evolving our approach from simple linear models to sophisticated state-space models, Jane Street has consistently remained at the cutting edge of machine learning innovation.  When you’re not working on your project, you will have plenty of time to use our office amenities, physical and virtual educational resources, attend guest speakers and social events, and engage with the parts of our work that excite you the most.

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind, a collaborative spirit, and a passion for solving interesting problems, we have a feeling you’ll fit right in.

About the Position

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.

Researchers at Jane Street are responsible for building models, strategies, and systems that price and trade thousands of financial instruments algorithmically. This job involves processing petabytes of data, produced by adversarial markets, that evolve everyday. Signals are small, noise is high.

We’re looking for people with advanced machine learning experience in either an applied or academic context. A good candidate should have a deep understanding of a wide variety of ML techniques, and a passion for iterating with model architectures, feature transformations, and hyperparameters to generate robust inferences. We move fast, and want people with the ability to quickly absorb the context of a new problem, carefully consider tradeoffs, and recommend possible solutions. 

You'll learn how Jane Street applies advanced machine learning and statistical techniques to model and predict moves in financial markets. Through a series of classes and activities, you will analyze real trading data via access to our growing GPU cluster containing thousands of A/H100s. You'll gain an understanding of the differences between textbook machine learning and its application to noisy financial data.

Note that given the IP sensitive nature of machine learning research at Jane Street, it is highly unlikely any research findings associated with the JS internship will be suitable for outside academic publication.

About You

We don’t expect you to have a background in finance — we’re more interested in how you think and learn than what you currently know. You should be:

• An undergraduate, PhD student, or postdoc with practical experience working on ML problems
• Able to apply logical and mathematical thinking to all kinds of problems
• Intellectually curious — asking great questions is more important than knowing all the answers
• A strong programmer in Python 
• An open-minded thinker and precise communicator who enjoys interacting with colleagues from a wide range of professional backgrounds and areas of expertise
• Eager to ask questions, admit mistakes, and learn new things
• Fluency in English required

Please note: Jane Street will provide flights to and from Hong Kong, as well as accommodation throughout the entirety of the program.

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Last updated on Aug 26, 2024