Machine Learning Internship - PhD: 2027

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team.

You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems.

You will leverage vast and diverse datasets and apply cutting-edge machine learning at scale to drive data-informed decisions in predictive modeling to strategic execution.

Successful intern candidates will participate in a U.S. based internship with full-time placement in our Hong Kong office upon graduation.

Visa sponsorship is available for this position.

• Conduct research and develop ML models to identify patterns in noisy, non-stationary data
• Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation
• Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches
• Design and run experiments using the latest ML tools and frameworks
• One-on-one mentorship from experienced researchers and technologists
• Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices
• Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior
• Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making
• Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
• Proven experience applying machine learning techniques in a professional or academic setting
• Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR