Quantitative Research Intern

Opportunities available in Chicago, Sydney, and Amsterdam 

At IMC, Quant Research Interns work at the intersection of advanced machine learning techniques and high-impact, real-world trading strategies. Our internship program is designed to allow you to apply your research skills in a fast-paced, innovative environment. As part of our team, you will work on developing predictive models using large-scale data analyses, pushing the boundaries of our research methodology. You will collaborate with experienced researchers, traders, and engineers, all while receiving mentorship from a senior member of our team. 

Your Core Responsibilities:

• Conduct hands-on research to design, develop, and apply original machine learning algorithms, with the autonomy to explore and innovate
• Apply your experience in at least one of the following techniques: Deep Neural Networks, Reinforcement Learning, Natural Language Processing (NLP), Time Series Analysis, Anomaly Detection, and Pattern Recognition
• Architect, prototype, and refine predictive models and trading strategies for robustness and scalability 
• Analyze large, complex datasets to uncover statistically significant patterns and market behavior insights
• Communicate research progress and outcomes effectively, ensuring knowledge transfer within the team and across departments
• Collaborate closely with a multidisciplinary team to enhance IMC’s machine learning capabilities and trading strategies

Your Skills and Experience: 

• Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Mathematics, Statistics, Physics, or related field of study
• Must be able to intern starting June 2025 and open to commence full-time employment upon graduation in 2026 or 2027 
• Deep understanding of machine learning, probability, and statistics, with practical experience in areas such as time series analysis, pattern recognition, portfolio optimization, market impact modelling and NLP
• Proven ability to conduct original research and deliver impactful results
• Extensive experience with modern deep learning architectures, with a solid foundation in classic machine learning techniques
• Strong programming skills in Python required
• Demonstrated experience with C++ a plus 
• Experience with multi-modal, multi-domain learning, and online learning is highly desirable
• Familiarity with analyzing large, unstructured, and heterogeneous datasets
• Theoretical and hands-on experience with time series forecasting is a plus