Campus Full Time 2026 - Quantitative Trader

THE ROLE:

XTY LABS AI RESIDENCY PROGRAM

XTY Labs is seeking AI Residents with a history of significant contributions to research in elite academic settings, such as PhDs, post-docs, or professorships. Exceptional machine learning researchers seeking to explore how their expertise can revolutionize the finance industry are particularly encouraged to apply. Additionally, we will also consider outstanding academics from other fields such as pure math, physics or similar.

No prior experience in finance is needed.

Full-time, (up to) year-long residency program is created to provide elite researchers with the freedom, guidance, and resources to create cutting-edge machine learning solutions tailored for the complexities of finance. Top-performers from the program will have the opportunity to transition into the core quantitative team at XTX in London.

We are currently only considering applicants who are looking to start from May 2025 onward.

XTY Labs offers two pathways within its AI Resident Program, designed to accommodate professionals at varying stages of their research careers. The roles are bifurcated into Junior AI Residents and Senior AI Residents, each tailored to align with the applicants' research experience and expertise.

This role is ideal for emerging researchers, including PhD candidates nearing the completion of their studies (less than one year from graduation) and those who have recently earned their PhD degrees. A Junior AI Resident role spans a minimum of 6 months with a preference for 12 months
.

The Senior AI Resident position is crafted for researchers with a substantial track record of experience beyond their PhD. This includes individuals who have held positions such as tenure-track or research faculty, or other senior research roles, and have a few years of demonstrable experience in leading research projects. A Senior AI Resident role spans a minimum of 12 months
.

AI Residents will be organized into focused cohorts, working in close collaboration with the Research Director Dr. Atlas Wang and the existing XTX quantitative research team to pinpoint the most impactful research questions. They will benefit from thorough mentorship and hands-on assistance from experienced team members, guaranteeing that their objectives are aligned, and their pace of progress is on track.

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 what you do best — advancing the field of machine learning in finance.