Machine Learning Internship - PhD: 2027

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

Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow

Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

Work with a world-class team of researchers and technologists

Access to unparalleled financial data and computing resources

Opportunity to make a direct impact on trading performance