Machine Learning Internship - PhD: 2027

About the role

The Machine Learning PhD Internship at Susquehanna is a 10-week immersive experience for PhD candidates dedicated to solving high-impact problems at the intersection of data, algorithms, and markets.

Responsibilities

• 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.

Requirements

• 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.

Qualifications

PhD candidate status is required.

Skills

• Strong programming skills in Python, R, or other relevant languages.
• Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch).
• Knowledge of statistical analysis and data preprocessing techniques.
• Ability to work independently and collaboratively in a dynamic team environment.
• Excellent communication and presentation skills.

Benefits

One-on-one mentorship from experienced researchers and technologists.

Pay

Competitive compensation package based on experience and qualifications.

Schedule

Full-time internship lasting 10 weeks.

Benefits

Flexible working hours, professional development opportunities, and access to cutting-edge financial data and computing resources.