Machine Learning Engineer

We’re looking for smart and curious individuals from academia 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 thousands of H100s/200s. Trading poses unusual challenges—extreme latency constraints, large datasets, complex feedback loops and a high level of noise—that force us to search for novel tricks.

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 ecosystem and understanding of varying approaches to shape decision-making as we continue building the future of ML at Jane Street. You’ll also be involved with 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 You

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 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 have:

• Practical experience working on real-world ML problems
• Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
• A strong mathematical background; good candidates will be excited about things like optimisation theory, regularisation techniques, linear algebra and the like
• A passion for keeping up with the state-of-the-art, whether that means diving into academic papers, experimenting with the latest hardware or reading the source of a new machine learning package
• A proven ability to create and maintain an organised research codebase that produces robust, reproducible results while maintaining ease of use
• Expertise wrangling an ML framework—we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow or others
• An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools

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