Deep Learning Research Intern - 2027 - Sydney

ROLE OVERVIEW

Our Machine Learning Internship is designed for curious, ambitious researchers who want to apply machine learning to complex, real-world problems. Over 10–12 weeks, you'll work alongside experienced researchers and mentors to develop models, analyze large-scale datasets, and contribute to research that informs IMC's trading strategies across global equities, futures, and options markets. You'll gain hands-on experience designing experiments, evaluating novel approaches, and tackling challenging problems in a collaborative, fast-paced environment where your work can have real-world impact.

Throughout the program, you'll deepen your understanding of quantitative trading through a combination of classroom and on desk training, while benefiting from professional development and networking opportunities. We offer a highly competitive compensation package, including travel and accommodation. High-performing interns may be considered for a full-time Graduate Researcher position upon graduation.

YOUR CORE RESPONSIBILITIES:

• Conduct hands-on research to design, develop, and apply original machine learning algorithms, with the support to explore and innovate.
• Analyze large-scale datasets, develop predictive models, and evaluate novel approaches to complex market problems
• Develop your research skills through hands-on project work, mentorship, and regular feedback from experienced researchers
• Enhance your understanding of quantitative trading through classroom-based instruction in options theory, market making, and related topics

YOUR SKILLS AND EXPERIENCE:

• Pursuing a 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

You may submit one application per role each year.  We strongly encourage you to focus on applying to a single role that best matches your skills and interests.  Though you may apply to multiple roles, please note that each application will be evaluated based on the specific criteria established for that particular role.

If you have already applied for this position during the current recruitment season and were not selected, you may reapply when the next recruitment season begins in 2027.