What does ML look like here?
Jane Street is a quantitative trading firm that trades hundreds of billions of dollars a day on more than 200 venues in 45 countries.
Our machine learning team works on the neural network models driving our trading strategies, and builds the infrastructure that make training and inference possible. We’re looking for smart and curious individuals from industry and academia to shape the future of ML at Jane Street.
In contrast to more typical text or vision settings, financial market data has low signal to noise; trading is dynamic – market participants adapt to each other’s actions; and there are often extreme latency constraints: imagine computing a forward pass in under a microsecond! To solve these problems, we rely on the talent of the researchers and engineers who work here, state of the art machine learning techniques, and a cutting-edge computing cluster with thousands of H100s/200s and growing.
What are the open roles in ML?
ML Researchers build the models driving our trading strategies. This work includes experimenting with new architectures, refining existing models, and integrating models into trading strategies. Apply here
ML Engineers develop the platforms and libraries enabling research and inference. This is a broad mandate, and includes designing GPU and job orchestration systems, writing market-specific feature engineering code alongside traders and researchers, and profiling/tuning training inner loops to make the best use of our clusters. Apply here
ML Performance Engineers specifically focus on optimizing the throughput and latency of our models for production inference and research. This work includes low-latency inference in production, training-loop profiling and optimization, and high-throughput inference for research using various technologies like CUDA, Triton, and specialized hardware accelerators. Apply here
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
What are the opportunities for PhD students?
We pursue cutting-edge 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
ML Interns are paired with full-time mentors, collaborating on real-world projects and learning 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, they analyze real trading data via access to our growing GPU cluster containing thousands of H100s/200s. Over the course of the program, interns will gain an understanding of the differences between textbook machine learning and its application to noisy financial data.
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