The Firm
XTX Markets is a leading algorithmic trading firm which uses state-of-the-art machine learning technology to produce price forecasts for over 50,000 financial instruments across equities, fixed income, currencies, commodities and crypto. It uses those forecasts to trade on exchanges and alternative trading venues, and to offer differentiated liquidity directly to clients worldwide. The firm trades over $250bn a day across 35 countries and has over 250 employees based in London, Singapore, New York, Paris, Bristol, Mumbai and Yerevan.
We leverage the talent of the people who work here, modern computational techniques and state-of-the-art research infrastructure to analyse large data sets across markets quickly and efficiently, to maximize the effectiveness of our proprietary trading algorithms. We are actively seeking new methods and ideas. The models that drive our trading strategies have evolved considerably over the last 10 years, from econometric methods that gave our company its name, to trees, to neural networks, to modern deep learning architectures.
XTX Markets has an unrivalled level of computational resources in the trading industry, with a growing research cluster currently containing over 25,000 GPUs with 650 petabytes of usable storage. Teams across the firm include world-class researchers with backgrounds in pure math, programming, physics, computer science and machine learning. The firm is also constructing a large-scale data centre in Finland to future-proof its significant computational capabilities.
At XTX Markets technology is our business and we are a diverse organization which attracts outstanding talent from across all industry backgrounds. We are focused on teamwork and our people collaborate on all aspects of the business, working openly and with respect for each other, our clients and the market. Our culture is non-hierarchical and one where everyone is valued. We strive for excellence in everything we do.
XTY Labs
XTY Labs, a division of XTX Markets established in 2024, stands at the intersection of finance and cutting-edge machine learning research. The lab is designed to be a home for those eager to pioneer the future of algorithmic trading, with a strategic focus on developing and applying novel machine learning techniques to financial data.
Situated in Hudson Yards, New York City, XTY Labs offers a state-of-the-art, “academia-like” workspace designed to inspire creativity and innovation. Led by Research Director Dr. Atlas Wang [Link], researchers will have the opportunity to explore new ideas and transform their advanced machine learning research into real-world financial solutions, backed by XTX Markets' expansive research cluster, rich datasets, and advanced technological infrastructure.
Emphasizing practical application, XTY Labs’ mission is to swiftly integrate successful models into the market, directly enhancing XTX's trading strategies and operations. This direct pipeline from research to implementation establishes XTY Labs as a premier destination for those looking to make a significant impact in the financial sector through original machine learning research.
XTY Labs offers an AI residency program, designed for exceptional researchers with a strong track record of contributions in top academic settings - including PhD graduates, postdocs & faculty members. This full-time, one-year residency provides elite researchers with unparalleled freedom, world-class mentorship, and cutting-edge resources to develop novel machine learning solutions tailored to the complexities of finance. We foster a uniquely flat, collaborative, and innovation-driven environment, free from bureaucracy, so you can focus entirely on pushing the boundaries of AI research in finance. Top-performing residents will have the opportunity to transition into full-time roles within XTX’s core quantitative research team.
Learn more about the residency program here: https://www.xtxmarkets.com/career/xty-labs-ai-residency/
The XTY AI Summer PhD Research Internship
Following the first year’s success of XTY Lab (currently a team of 7 leading researchers generating impactful research for the firm), we’re excited to announce our AI Summer PhD Research Internship.
This 12–14-week research-focused internship, starting in May or June 2025, is designed for highly motivated PhD students in the middle years of their doctoral training, who are eager to push the boundaries of high-risk, high-reward AI research and rapidly integrate cutting-edge ideas into our advanced strategies.
Why Join?
This is an exclusive opportunity for PhD students who excel in research-driven environments and are eager to make a meaningful impact in a field still rich with untapped potential in the GenAI era. Our environment is designed for innovation with a flat organizational structure, a friendly and collaborative culture, and an absence of red tape, allowing you to focus on advancing the field of machine learning in finance.
Responsibilities
The program offers an unparalleled opportunity to work within a rich computational environment, equipped with an impressive array of resources. Residents will immerse themselves in a culture that promotes collaboration, innovation, and a flat hierarchy. The program is designed to bring together a wide range of backgrounds and interests, not only those who are already set on a career in finance.
Essential Attributes
Desirable Attributes
Compensation
The base salary for the AI Summer PhD Research Internship role will be $30,000 per month. You will work from our NY office at least three (3) days per week and will enjoy similar benefits to those of our US permanent employees as appropriate.
Interview Format
We plan to keep our internship cohort small, with a highly selective application process. Our interview process seeks to gain signal in the following topics:
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.
Your interview process will follow a three-stage format:
To apply, please include your CV/Resume, the 2 papers you believe best showcase your research ability, and a short accompanying commentary on why you chose those papers.