ML Research Intern: Build Predictive Models for Global Markets

IMC Trading; Sydney; On-site; AUD 42,000 - 66,000; Full time

Benefits offered by this job

Travel provided

Accommodation provided

Mentored research

Job summary

IMC Trading offers a Machine Learning Internship for 10–12 weeks, where you will work with experienced researchers to design models, analyze datasets, and contribute to research informing trading strategies across equities, futures, and options markets.

You’ll gain hands-on experience testing novel approaches, conducting experiments, and learning quantitative trading concepts in a collaborative, fast-paced environment.

Qualifications

• Pursuing a PhD in ML, CS, EE, math, statistics, physics, or a related quantitative field.
• Strong foundations in ML, 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.
• Publications, preprints, research internships, or significant research projects; publications at venues like NeurIPS, ICML, ICLR preferred.

Responsibilities

• Conduct hands-on research to design, develop, and apply original machine learning algorithms, with 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

Skills

PhD candidate

Machine Learning

Python

Deep Learning

Research experience

Education

PhD in a quantitative field

Tools

PyTorch

TensorFlow

JAX

Job description

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.

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.

About Us

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.