2027 Machine Learning Center of Excellence (Quant AI) - Summer Associate

JPMorganChase•Palo Alto, CA

About The Position

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase accelerates the firm’s data and analytics journey. Within CDAO, the Machine Learning Center of Excellence (MLCOE) partners across the firm to shape, create, and deploy machine learning solutions for challenging business problems. Its work includes supporting data quality, integrity, and security, and using data to generate insights and inform decision-making. CDAO also develops solutions that support the firm’s commercial goals by applying AI and machine learning to create products, improve productivity, and enhance risk management responsibly. As a Quant AI Summer Associate within MLCOE, you will join technologists, machine learning researchers, and engineers who explore current research and apply machine learning techniques to JPMorgan’s data. The team collaborates with traders, portfolio managers, quants, data scientists, and salespeople to support data-led business transformation. The Summer Associate Internship Program begins in June, depending on your academic calendar. The program supports professional growth through project work related to academic and professional interests, mentorship, and speaker series with senior leaders. Full-time employment offers may be extended after successful completion of the program.

Requirements

Be enrolled in a PhD or MS program in Computer Science, Physics, Mathematics, Engineering, or a related quantitative field, with an expected graduation date from December 2027 through August 2028.
Have solid programming skills in C/C++, Python, Java, or equivalent languages.
Have deep knowledge of machine learning, data mining, applied mathematics, optimization, and statistics.
Have knowledge of machine learning and deep learning toolkits such as TensorFlow, PyTorch, or JAX, and ETL pipelines, including batch and real-time data processing.
Demonstrate scientific thinking and the ability to design experiments and training frameworks, and to define and evaluate intrinsic and extrinsic model-performance metrics aligned with business goals.
Have strong written and spoken communication skills to communicate technical concepts and results to technical and business audiences.
Be able to work independently and in highly collaborative team environments.

Nice To Haves

Interest in quantitative finance and/or portfolio management.
Knowledge of CPU and GPU architecture, high-performance computing, and XLA compilers.
Experience in distributed system design and development.

Responsibilities

Research and explore new machine learning methods through independent study, attendance at industry conferences, experimentation, and participation in the knowledge-sharing community.
Develop state-of-the-art machine learning models to solve real-world problems and apply them to different tasks.
Collaborate with Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management partner teams to deploy solutions into production.