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

JPMorgan Chase · Banking · LONDON, United Kingdom · Corporate Sector

JPMorgan Chase is seeking a 2026 Machine Learning Center of Excellence Summer Associate for a 6-month internship focused on Time Series & Reinforcement Learning. The role involves applying advanced ML techniques to financial problems, conducting independent research, and collaborating with mentors and business professionals. Requires a PhD in a quantitative discipline, strong programming and ML/RL knowledge, and experience with Python and ML frameworks like TensorFlow and JAX.

What you'd actually do

Create strategically important AI/ML application in the Chief Data & Analytics office.
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.

Skills

Required

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.
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.

What the JD emphasized

PhD graduation between December 2026 and August 2027
Published research in machine learning, science, engineering, quantitative psychology, business, or related fields

Other signals

advances state-of-the-art methods
solve a wide range of real-world financial problems
utilize advanced machine learning techniques
independent research
demonstrate deep passion for machine learning
robust expertise in deep learning
learning, researching, and experimenting with innovations
design experiments and training frameworks
define and evaluate model performance metrics

Read full job description

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.

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.