Graduate Machine Learning Researcher

Conduct advanced machine learning and quantitative research to develop, test, and deploy models that improve global trading strategies across equities, futures, and options.

As a Graduate Machine Learning Researcher at IMC, your work will directly impact our 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.

Your Core Responsibilities:

• 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

Your Skills and Experience:

• PhD in Machine Learning, Computer Science, Electrical Engineering, Mathematics, Statistics, Physics or a related quantitative field

• Strong foundations in machine learning, probability, and statistics, with experience applying advanced ML techniques to solve challenging research or real-world problems

• Demonstrated hands-on research experience in deep learning fundamentals such as neural network architectures, sequence modeling, training dynamics, or optimization

• Proficiency in Python and modern machine learning frameworks such as PyTorch, Tensorflow, and/or JAX

• Demonstrated research excellence through publications, preprints, research internships, or significant research projects; publications at venues such as NeurIPS, ICML, ICLR, or equivalent conferences are highly preferred

• Ability to commence full-time employment in February or August 2027