Our goals are to give you a real sense of what it’s like to work at Jane Street full time as a Machine Learning Researcher, and a truly unparalleled educational experience. You’ll work side by side with experienced ML Researchers on projects that we’ve selected for their combination of novel ML ideas and relevance to real-world systematic trading strategies. You'll learn how we think about markets through challenging classes and activities, and practice using established methods alongside our own unique twists to train practical models.
At Jane Street, the lines between research, technology, and trading are intentionally blurry. As our strategies grow more sophisticated, close collaboration is essential for continuing to push the boundaries of what’s possible. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. Trading poses unusual challenges—large models and nonstationary datasets in a competitive multi-agent environment—that force us to search for novel techniques.
You’ll spend the bulk of your internship working closely on projects with experienced ML Researchers at Jane Street. You might conduct an end-to-end study of an unexplored dataset, try a new modelling paradigm for a thorny problem, or consider blue-sky approaches that we’re still trying to figure out. The problems we work on rarely have clean, definitive answers, and they often require insights from colleagues across the firm with different areas of expertise. Depending on the day, you might be diving deep into market data, tuning hyperparameters, debugging training issues, or analyzing the predictions your model makes.
Note that given the IP-sensitive nature of machine learning research at Jane Street, it is unlikely that any research findings associated with the internship will be suitable for outside academic publication.
About youIf 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 and a passion for solving interesting problems, we have a feeling you’ll fit right in. You should be:
- A PhD student or postdoc with practical experience working on empirical ML research problems
- Interested in applying logical and mathematical thinking to all kinds of problems
- Curious about the machine learning landscape and excited to apply state-of-the-art techniques drawn from many problem domains
- Fluent with a versatile set of models and tricks
- Able to rapidly implement and iterate on your ideas in Python and your favorite ML framework
- Eager to ask questions, admit mistakes, and learn new things
- Fluent in English
If you’d like to learn more, you can read about our interview process and meet some of the team. Learn more about Jane Street’s internship program here.
Top Skills
What We Do
Jane Street works differently. As a liquidity provider and market maker, we trade on more than 200 trading venues across 45 countries and help form the backbone of global markets. Our approach is rooted in technology and rigorous quantitative analysis, but our success is driven by our people.
Our bright, beautiful offices in the heart of New York, London, Hong Kong, and Amsterdam are open and buzzing with conversation. We come from many backgrounds and encourage travel between offices to share perspectives. Some of our best ideas come from bumping into a visiting colleague at the office coffee bar.
Markets move fast. Staying competitive as we’ve grown has required constant invention—of new trading strategies, technology, and processes. We’ve found this is easier when you hire humble, kind people. They tend to help each other, and prioritize teamwork over titles.
We invest heavily in teaching and training. There’s a library and a classroom in every office, because deepening your understanding of something is considered real work. Guest lectures, classes, and conferences round out the intellectual exchanges that happen every day.
People grow into long careers at Jane Street because there are always new and interesting problems to solve, systems to build, and theories to test. More than twenty years after our founding, it still feels like we’re just getting started.









