Machine Learning Researcher at IMC Trading

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC.  The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism.  This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.

Your Core Responsibilities:

• Design and deploy machine learning models to enhance trading performance across various asset classes
• Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
• Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
• Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Your Skills and Experience:

• PhD or Master’s in Engineering, Math, Statistics, Computer Science, or related quantitative field
• 2+ years of experience building applied ML models; previous experience in trading environment preferred
• Proven expertise in developing and deploying predictive models
• Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
• Strong understanding of theoretical foundations of state-of-the-art ML models
• Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
• Ability and desire to work in a collaborative team environment
• Excellent written and verbal communication skills