Machine Learning Engineer

1 Week ago • All levels

Job Summary

Job Description

The Machine Learning Engineer will be responsible for building deep learning models to power trading strategies. This involves working with large datasets, addressing latency constraints, and managing complex feedback loops. The role requires collaboration with researchers, engineers, and traders to train models, design systems, and execute trading strategies. The engineer will also be involved in hiring, attending conferences, and teaching teammates. A strong understanding of the machine learning ecosystem is essential, as well as the ability to adapt to novel challenges in the finance industry. The engineer should have the ability to move from concept to production, keep up with the state-of-the-art, and create an organized, reproducible research codebase.
Must have:
  • Practical experience with real-world ML problems.
  • Experience building and maintaining training and inference infrastructure.
  • Strong mathematical background.
  • Passion for keeping up with the state-of-the-art.
  • Ability to create and maintain an organized research codebase.
  • Expertise with an ML framework like PyTorch, Jax, or TensorFlow.
  • Inventive approach and willingness to ask questions.
  • Fluent in English.

Job Details

About the Position

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
  • Fluent in English

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

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About The Company

Jane Street is a quantitative trading firm with offices in New York, London, Hong Kong, Singapore, and Amsterdam. We are always recruiting top candidates and we invest heavily in teaching and training. The environment at Jane Street is open, informal, intellectual, and fun. People grow into long careers here because there are always new and interesting problems to solve, systems to build, and theories to test.



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