We’re looking for smart and curious individuals to join our growing team and drive our ML work.
On our Machine Learning team, you'll build the deep learning models that power our trading strategies, supported by our rapidly growing computing cluster with tens of 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.
At Jane Street, our researchers, engineers, and traders sit a few feet away from each other and work together to train models, architect systems, and run trading strategies. Depending on the day, we might be diving deep into market data, tuning hyperparameters, debugging distributed training performance, or studying how our model likes to trade in production.
We’ll rely on your in-depth knowledge of the machine learning landscape and understanding of a variety of approaches—drawn from LLMs, image models, RL agents, recommendation systems, or classical ML methods—to shape the future of ML at Jane Street. You’ll train models for the next generation of our deep learning-based trading strategies, and build the fundamental understanding we need to tackle new markets and situations. You’ll also be hiring new colleagues, attending conferences, and teaching techniques to teammates—all of which we consider to be real and impactful parts of the job.
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. There’s no fixed set of skills we are looking for, but you should bring:
- Practical experience working on empirical ML problems
- The ability to apply logical and mathematical thinking to all kinds of problems
- Intellectual curiosity and excitement about state-of-the-art research across many ML problem domains
- Fluency with a versatile set of models and tricks
- The hands-on coding skills needed to rapidly implement and iterate on your ideas, in Python and your favorite ML framework
- An eagerness to ask questions, admit mistakes, and learn new things
If you’d like to learn more, you can read about our interview process and meet some of the team.
Skills Required
- Practical experience working on empirical ML problems
- Ability to apply logical and mathematical thinking
- Intellectual curiosity in state-of-the-art ML research
- Fluency with various ML models and tricks
- Hands-on coding experience in Python and ML frameworks
Jane Street Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Jane Street and has not been reviewed or approved by Jane Street.
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Career-Linked Recognition & Rewards — Pay is considered exceptionally strong across roles, with substantial bonuses in strong years and firm-wide performance sharing. Collaboration is expected and rewarded, reinforcing a team-oriented payout model.
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Healthcare Strength — Benefits include zero-premium medical, dental, and vision coverage in the U.S., plus access to on-site or concierge primary care, physical therapy, and mental health services. This breadth and convenience signal a robust healthcare offering.
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Parental & Family Support — Paid parental leave is described as generous, with equipped nursing rooms and backup childcare cited. Family-oriented supports like fertility coverage and elder‑care backup further enhance the package.
Jane Street Insights
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.
