Quantitative Researcher - Deep Learning
On-site · London, England, United Kingdom
Job Summary
Quantitative Researcher - Deep Learning at XTX Markets. Lead original research to improve forecasting and trading systems using deep learning for large-scale time-series and cross-sectional prediction. Design and train deep learning models, improve representations and objectives, investigate optimization and scaling, and enhance robustness under distribution shift. Collaborate with engineers and trading technologists to deploy research into production systems, learn market structure to refine modeling questions, and contribute to state-of-the-art ML research on challenging data with substantial compute and infrastructure. The role emphasizes rigorous experimentation, mathematical depth, strong programming practice, and the ability to challenge ideas in a flat team environment. Beneficial experience includes PhD or equivalent research, experience training large models, and work in time-series modelling, online/continual learning, reinforcement learning, causal inference, probabilistic modelling, optimisation, HPC, or low-latency systems.
Required Qualifications
- Evidence of original technical ability in ML, statistics, mathematics, computer science, physics or another quantitative field
- Deep practical understanding of modern deep learning: architectures, objectives, optimisation, scaling, evaluation and failure modes
- Strong mathematical and statistical foundations
- Excellent programming ability and comfort working close to the details of implementation
- Good experimental taste: forming hypotheses, designing clean tests, reading ambiguous results and changing your mind quickly when the evidence changes
- High standards, intellectual honesty and the ability to work in a flat team where ideas are challenged directly
- No finance background is needed; willingness to learn the domain
- Beneficial Experience: PhD or equivalent research experience in a relevant technical field
- Experience training large models or building ML systems at meaningful scale
- Work in time-series modelling, online or continual learning, reinforcement learning, causal inference, probabilistic modelling, optimisation, compilers, HPC or low-latency systems
- Experience taking research beyond a prototype
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