XTX Markets is an algorithmic trading firm that actively trades tens of thousands of instruments on over eighty venues with a daily volume of almost three hundred billion USD. Trading algorithms are developed by the quant team, which consists of fewer than twenty researchers and a research cluster of a hundred thousand cores and ten thousand A/V100 GPUs (and growing fast). The algorithms we develop are necessarily extremely general and robust. The culture is collaborative and friendly. The team structure is flat. There is no bureaucracy.

Trading provides a unique set of challenges for machine learning research. Datasets contain trillions of observations. Markets constantly adapt. They react rapidly, often adversarially, to trades and, over longer horizons, to new trading strategies. Algorithms must be as fast as possible: every microsecond matters when reacting to new market information. At the same time, it is necessary to account for risks associated with holding periods that can be weeks – a range of timescales that spans twelve orders of magnitude.

The models that drive our trading strategies evolved considerably over the last 10 years, from econometric methods that gave a name to the company, to trees, to neural networks, to modern deep learning architectures. We are actively seeking new methods and ideas.

• Degree in math, physics, computer science or another quantitative field
• Strong evidence of ability to conduct original research
• Demonstrated practical experience of, or deep understanding of, modern deep learning architectures
• Good knowledge of other machine learning techniques and statistics
• Solid programming skills (language does not matter)

• Track record of solving practical and analytical problems
• Outstanding performance in any quantitative field or contest (Kaggle, hackathons, olympiads, academic contests etc)
• Experience implementing machine learning algorithms in industry

Our interview process seeks to gain signal in the following topics:

• Modern machine learning techniques, especially deep learning
• Classical machine learning techniques
• Probability theory
• General mathematics (linear algebra, analysis etc)
• Computer science / algorithms

Your process will follow the following format:

• Phone or video call with a quant researcher covering the above topics. You may have a second if we do not cover all the topics
• Practical assignment - we provide a dataset, and this exercise is about building a model end to end
• Onsite interview - there will be 10 interviewers, and you will be interviewed on each topic twice

Success Criteria:

• We seek to get a broad signal in our process. Successful candidates are those who are strong in all areas and exceed expectations in at least one

APPLICATIONS:

[email protected]

In your application email, please include details of your most outstanding achievement (e.g. paper, project, proof, code). Explain your contribution and why you consider it impressive.