Exploring new frontiers in machine learning

“A lot of the problems that I work on feel like very new ground, and I’d be surprised if we weren’t some of the first people in history to be solving these problems.”

- Declan, ML Researcher

Think of Jane Street as a research lab with a trading desk attached to it

We believe deep learning is the future of algorithmic trading. To get us there, our machine learning team creates the neural network models driving our trading strategies, and builds the infrastructure that make training and inference possible.

What’s unique about machine learning at Jane Street?

We break new ground in predictive modeling, developing approaches that have never been tried before. Here’s some of what we’re up against:

  • Financial markets produce a torrent of data, mostly noise.
  • We need ultra-low latency systems just to process it.
  • Market data is “regime-y,” in that it undergoes frequent structural changes in reaction to things like pandemics, elections, new regulations—even swings in the collective mood.
  • Identifying these distinct distributions can be tricky, let alone learning against them.
  • And just when we think we’ve got it, our own actions influence the data we’re trying to model (itself a very fun problem to think about).

Thousands

of H100s/200s and growing

$
190

billion

daily filled dollars

323
 petabytes

current storage

What does machine learning look like at Jane Street?

  • No silos. Researchers, engineers, and traders sit a few feet away from each other and work together to train models, build systems, and run trading strategies.
  • Depending on the day, we might dive deep into market data, tune hyperparameters, debug distributed training, or study our model’s trades in production.
  • We build on the latest papers in LLMs, computer vision, RL, training libraries, cuda kernels, or whatever else we need to train good models.
  • We invent our own set of architectures, tricks, and optimizations that work for trading.

Opportunities

Help us pioneer the future of finance.

Open Roles

ML Researchers invent and reinvent the methods and models driving our trading strategies.

ML Engineers bring our models to life with the best tools for the job.

ML Performance Engineers speed up our models, automate them, and manage the care and feeding of our training loops. 

Programs and Events

Graduate Research Fellowship

A year’s worth of tuition and fees, plus a
$50,000 stipend.

Machine Learning
Research Internship

Direct mentorship, real-life projects,
and a week abroad.

Resources

Podcast

The Uncertain Art of Accelerating ML Models

See how Sylvain Gugger speeds up models, and
keeps hungry GPUs well-fed.

Blog Post

A New Way to Visualize Piecewise-Linearity

One Streeter animated the output of a neural net. It may look familiar.

Video

Meet Matt at Jane Street

He’s helped us grow our ML team since 2012.

PDF

Machine Learning Interview Guide

Podcast

Signals and Threads: All Episodes

Welcome Dwarkesh Fans,

We’re Dwarkesh fans too and we’re pleased to be an ongoing sponsor of his content. Learn more about what we are doing in the area of machine learning.

Neural networks are powerful black box models, but at Jane Street, we like to know what’s going on inside. Can you crack our neural net puzzle?