Jane Street
Machine Learning Researcher
About this role
Join Jane Street's Machine Learning team to develop deep learning models powering trading strategies using state-of-the-art techniques and massive GPU infrastructure. Work collaboratively with researchers, engineers, and traders to solve novel ML challenges in a competitive, multi-agent trading environment while advancing cutting-edge research.
What you'll do
- Build and train deep learning models for next-generation trading strategies
- Debug and optimize distributed training performance across large GPU clusters
- Analyze market data and study model behavior in production trading scenarios
- Mentor new team members and share ML techniques across the organization
- Conduct research on novel ML approaches tailored to trading challenges
- Attend conferences and stay current with state-of-the-art ML research
What they're looking for
- Deep learning model development and training
- Python and ML frameworks (TensorFlow, PyTorch, etc.)
- Empirical machine learning problem-solving
- Mathematical and logical reasoning
- Distributed systems and GPU optimization
- Knowledge of LLMs, computer vision, reinforcement learning, and classical ML
- Rapid prototyping and iterative development
- Data analysis and model debugging
Benefits
- Access to tens of thousands of high-end GPUs for research
- Close collaboration with traders and engineers
- Exposure to novel ML challenges in finance
- Opportunities to shape ML strategy and direction
- Conference attendance and research publication support
- No finance background required
Opens the official application on the employer’s site. No login required.
Jane Street
Jane Street builds high-performance trading systems and critical infrastructure using primarily OCaml and Python to support distributed trading operations. The company is hiring software engineers, network engineers, and operations engineers at both intern and full-time levels to work on real production projects including trading systems, infrastructure optimization, routing protocols, and internal tooling.
- Website
- janestreet.com
Likely interview questions
- Walk us through a recent empirical ML project you worked on—what was the problem, your approach, and how did you evaluate success?
- Describe your experience with distributed training and optimizing deep learning models at scale. What challenges have you encountered?