Drawing on machine learning to advance quantitative research.
Machine learning has been a key part of Jane Street’s work from the beginning; we’ve leveraged a variety of modeling techniques since our founding in 2000. The depth of our reliance on these models has grown dramatically in the last few years as we’ve adopted ever more sophisticated techniques to improve and inform our trading. Traders and researchers at Jane Street build models, strategies, and systems that price and trade a variety of financial instruments. We analyze large datasets using a variety of machine learning techniques, exploring the latest theory and pushing beyond existing performance limits.
Curious people coming together to solve complex problems.
Our Machine Learning team works to build and refine platforms and infrastructure that have a wide‑ranging impact on the firm. We’re looking for smart, curious individuals to help us shape the future of machine learning at Jane Street.
ML Researchers are responsible for building models, strategies, and systems that price and trade a variety of financial instruments. A mix of trading and software engineering roles, this work involves analyzing large datasets, building and testing models, creating new trading strategies, and writing the code that implements them.
Our ML Engineers help drive the direction of an ML platform that is used daily by traders and researchers. The work is wide-ranging, including things like developing libraries for automating ML workflows and experiment evaluation, digging into the internals of open‑source ML tools, and optimizing our systems to match the needs of our trading systems.
ML Performance Engineers optimize the performance of our models. This work focuses on efficient large-scale training, low-latency inference in real-time systems and high-throughput inference in research. Engineers take a whole-systems approach, including storage systems, networking and host- and GPU-level considerations.
Be part of the solution
Trading on the world’s electronic markets is highly competitive, which has led us to pursue innovative research in machine learning, programmable hardware, compiler design, and more. The programs below are available to students pursuing PhDs in Machine Learning:
Graduate Research Fellowship (GRF)
The Fellowship supports exceptional doctoral students currently pursuing a PhD in computer science, mathematics, physics, or statistics. If accepted, you’ll receive a year’s worth of tuition and fees, as well as a $50,000 stipend to support you as you continue your research. Fellows will also be invited to visit our office to give a talk on any topic of their choosing to Jane Street employees and other Fellowship recipients.
Machine Learning Researcher Internship
Paired with full-time mentors, you’ll collaborate on real-world projects and 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.
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.
Jane Street Kaggle: Real-Time Market Data Forecasting
Predict financial market responders using real-world data.
Want a glimpse into the daily challenges of successful trading? We’ve just launched a new Kaggle competition so you can try your hand.
We hired the winner of our last Kaggle and he organized this one. This challenge highlights the difficulties in modeling financial markets, including fat-tailed distributions, non-stationary time series, and sudden shifts in market behavior. It has bigger data than our last competition, more sophisticated features, various auxiliary responders, and provides a lagged responder so participants can try with online learning, etc. There’s a variety of ways to play with the data in this competition and we want people to have fun!
1st Place: $50,000 |
2nd Place: $25,000 |
3rd Place: $10,000 |
4th - 10th Place: $5,000
