Location
London

Hours
Full-time internship, starting in June (dependent on academic calendar)

Salary
Competitive internship compensation

About the Role
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is dedicated to accelerating the firm's data and analytics journey. As part of CDAO, the Machine Learning Center of Excellence (MLCOE) partners across the firm to design, develop, and deploy machine learning solutions addressing complex business challenges. This includes ensuring data quality, integrity, and security while leveraging data to generate insights and support decision-making. The MLCOE harnesses artificial intelligence and machine learning technologies to develop innovative products, improve productivity, and enhance risk management effectively and responsibly.

As an intern within the MLCOE, you will apply advanced machine learning techniques to diverse domains such as natural language processing (NLP), large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. You will collaborate closely with MLCOE mentors, business experts, and technologists, conducting independent research and deploying solutions into production.

This internship offers a unique opportunity to contribute to and learn from a world-class machine learning team. Your project work will have direct impact on JPMorgan's businesses, be integrated into product pipelines, or contribute to published research in top AI/ML conferences. Professional growth is supported through mentorship, an engaging speaker series with senior leaders, and a collaborative environment. Successful completion of the internship may lead to full-time employment offers within a hybrid work model.

Experience
Enrolled in a PhD or MS program in a quantitative discipline such as Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields, or possess equivalent research or industry experience. Expected graduation between December 2026 and August 2027.

Strong background in NLP, large language models, speech recognition and modeling, or personalization/recommendation systems. Familiarity with state-of-the-art practices in these domains.

Proficient in Python and experienced with machine learning and deep learning toolkits such as TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas.

Scientific thinking with the ability to design experiments, training frameworks, and evaluate model performance metrics aligned with business goals.

Solid written and verbal communication skills to effectively convey technical concepts and results to both technical and business audiences.

Ability to work independently as well as collaboratively in team environments.

About You
Passionate about machine learning and committed to continuous learning and innovation.

Self-motivated with strong problem-solving skills and a collaborative mindset.

Interest in applying machine learning techniques to real-world business challenges within the financial services industry.

Qualifications
Strong foundation in Mathematics and Statistics is preferred.

Published research in natural language processing, deep learning, reinforcement learning, or related areas at major conferences or journals is an advantage.

Experience developing and debugging production-quality code.

Familiarity with continuous integration models and unit test development is desirable.