Graduate Machine Learning Researcher - London
IMC TradingAbout the Role
As a Graduate Machine Learning Researcher at IMC, your work will directly impact global trading strategies. You will apply advanced machine learning, statistical, and computational techniques to solve complex, real-world trading problems, contributing to trading strategies across global equities, futures, and options markets. Machine Learning Researchers work closely with Traders and Developers in an environment where problem solving, innovation and teamwork are recognized and rewarded.
- Design, implement, and evaluate machine learning models for trading applications, with successful research deployed into production
- Apply rigorous statistical techniques to measure performance, control overfitting, and understand model risk and interpretability
- Investigate novel machine learning approaches through rigorous experimentation and analysis in real trading environments
- Communicate research progress and outcomes effectively, ensuring knowledge transfer within the team and across departments
- Collaborate closely with traders, researchers, and engineers to translate research into production trading systems
Requirements
- A minimum of an MSc (PhD preferred) in Machine Learning, Statistics, Deep Learning, Probabilistic Programming, or a related quantitative field
- Currently in your final year of study, graduating in 2027
- Demonstrated experience in deep learning research or applied machine learning
- For PhD candidates, a record of research contributions in your area published at leading ML venues (e.g., NeurIPS, ICML, ICLR)
- For Bachelor’s and Master’s candidates, research experience is required but can take other forms, e.g. thesis work, open-source contributions, or projects
- Hands-on experience with modern deep learning architectures, such as transformers, sequence models, representation learning, self-supervised learning, and/or generative models
- Solid understanding of the theoretical foundations of deep learning, optimization, statistical learning, and neural network training dynamics
- Strong programming skills in Python and hands-on experience with at least one modern deep learning framework, such as PyTorch, JAX, or TensorFlow
- Strong communication skills with the ability to explain technical ideas clearly to both technical and non-technical audiences