Principal Research Scientist - Machine Learning

IMC is one of the world’s leading trading firms, combining quantitative research, technology and trading expertise to solve complex problems at scale. As Machine Learning becomes increasingly important within our Systematic Equities business, we are investing in frontier research that can shape the next generation of models, infrastructure and research capabilities. With access to world-class datasets, significant compute resources and a highly collaborative environment, IMC offers a unique opportunity to see cutting-edge research translated into real-world impact.

We are seeking a Principal Research Scientist to help define and accelerate our long-term machine learning research agenda. Prior finance experience is not required. The Role This role is designed for an established academic researcher or research scientist who wants to remain connected to the frontier of machine learning while applying their expertise in a highly impactful environment.

You will work alongside quantitative researchers, engineers and research leadership to identify emerging research directions, evaluate new developments in machine learning, and help translate promising academic advances into practical applications. Rather than owning individual trading strategies, you will act as a scientific leader, mentor and advisor across the research organisation. You will also represent IMC externally through participation in leading conferences and engagement with the broader machine learning community.

Shape the long-term machine learning research agenda within Systematic Equities.

• Identify and translate advances from frontier ML research into practical opportunities.
• Conduct original research and develop proof-of-concept solutions where appropriate.
• Advise and mentor quantitative researchers on methodology, experimentation and research direction.
• Help guide future investments in research tooling, infrastructure and compute capabilities.
• Represent IMC within the global machine learning community through conferences such as NeurIPS, ICML and ICLR.
• Contribute to publication efforts where appropriate and consistent with intellectual property considerations. Skills & Experience
• PhD in Machine Learning, Computer Science, Statistics, Mathematics, Physics or a related quantitative discipline.
• Outstanding academic credentials and a strong publication record in leading research venues.
• Demonstrable interest in Financial Markets is required.
• Current or recent experience in a faculty-equivalent academic or research leadership role such as Assistant Professor, Associate Professor, Professor, Group Leader, Principal Investigator, Senior Postdoctoral Researcher, or Senior Research Scientist.
• Deep expertise in modern machine learning and strong awareness of emerging research trends.
• Experience influencing research direction, mentoring researchers, or leading research initiatives.
• Strong programming skills and experience with modern machine learning frameworks.
• Excellent communication skills and the ability to collaborate across disciplines.

The opportunity to influence the future of machine learning at one of the world’s leading trading firms.

• Access to exceptional datasets, infrastructure and large-scale compute resources.
• Continued engagement with the academic community through conferences, collaborations and publication opportunities.
• A collaborative environment where research can move rapidly from idea to impact.
• Competitive compensation that reflects both scientific excellence and commercial impact.

IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets.

Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.

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