2026 Machine Learning Center of Excellence Summer Associate – Time Series & Reinforcement Learning Internship – (6 months)

The Machine Learning Center of Excellence (MLCOE) is a world-class machine learning team which continually advances state-of-the-art methods to solve a wide range of real-world financial problems using the company’s vast and unique datasets.

As a 2026 Machine Learning Center of Excellence Summer Associate within our dynamic team, you will be given the chance to utilize advanced machine learning techniques across a range of intricate domains such as finance, banking, economics, marketing, natural language processing, reinforcement learning, accounting, operations management,

Job responsibilities:

• Create strategically important AI/ML application in the Chief Data & Analytics office. Our work spans across all of J.P. Morgan’s lines of business including Commercial & Investment Bank, Asset Wealth Management, Consumer & Community Banking, and through every part of the organization from front office sales and trading to operations, technology, finance and more.
• Embrace opportunity to explore novel and complex challenges that could profoundly transform how the firm operates.
• Collaborate closely with our MLCOE mentors, business professionals, and technologists, carrying out independent research and providing solutions to the business.
• Demonstrate deep passion for machine learning, robust expertise in deep learning with practical implementation experience, and a dedication to learning, researching, and experimenting with innovations in the field.

Required qualifications, capabilities and skills

• • Enrolled in or completed a PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields), or equivalent research/industry experience. Post-doctoral candidates and career changers are also welcome.
  • Expected PhD graduation between December 2026 and August 2027, or already graduated.
  • Strong background in programming, mathematics, and statistics, including areas such as Stochastic Calculus, Bayesian techniques, State-Space Models, MCMC, and MCTS.
  • Published research in machine learning, science, engineering, quantitative psychology, business, or related fields.
  • Knowledge and experience in Machine Learning and Reinforcement Learning, along with familiarity with state-of-the-art practices and domains such as Finance, Economics, Accounting, Marketing, or Operations Research.
  • Proficiency in Python and experience with machine learning/deep learning frameworks such as TensorFlow, TensorFlow Probability, and JAX.
  • Scientific mindset with the ability to design experiments and training frameworks, and define and evaluate model performance metrics aligned to business objectives.
  • Ability to develop, debug, and maintain production-quality code, including familiarity with continuous integration and unit testing practices.
  • Strong written and verbal communication skills, with the ability to explain technical concepts and results to both technical and business audiences.
  • Innovative, curious, hardworking, detail-oriented, and motivated by solving complex analytical problems; able to work independently and in highly collaborative team environments.
  • Familiarity with the financial services industry and relevant certifications such as CFA, ACCA, PCAP, or other finance, accounting, programming, software engineering, Coursera, or equivalent professional qualifications.