Machine Learning Research Internship - PhD: 2027

Our Machine Learning Research 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 Research 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.

Susquehanna's proprietary datasets and computing infrastructure - including a rapidly growing cluster of thousands of high-end GPUs - support computationally intensive training, simulation, and rapid experimentation. Research teams here are small and highly collaborative, ideas are debated openly, and work that proves out reaches production quickly. 

What You Can Expect

• 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