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. Strategically positioned in the Chief Technology Office, our work spans across all of J.P. Morgan’s lines of business including Corporate & Investment Banking, 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. With this unparalleled access to the firm, this role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the firm operates.

As a Summer Associate within the MLCOE, we invite you to apply sophisticated machine learning methods to a wide variety of complex domains. This includes natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. We value collaboration and expect you to work closely with our MLCOE mentors, business experts, and technologists. Your role will involve conducting independent research and deploying solutions into production. We encourage you to have a strong passion for machine learning, solid expertise in deep learning with hands-on implementation experience, and a commitment to continuous learning and innovation in the field. This role provides a unique opportunity to contribute to and learn from a world-class machine learning team. Learn more about our MLCOE team at jpmorgan.com/mlcoe.

Our Summer Associate Internship Program begins in June, depending on your academic calendar. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, an engaging speaker series with our senior leaders and more. Your project will have direct impact on JPMorgan’s businesses, will be integrated into our product pipelines, or be part of published research in top AI/ML conferences. Full-time employment offers may be extended upon successful completion of the program within our hybrid work model.

 

Job responsibilities

Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production

 

Required qualifications, capabilities, and skills

Enrolled in a PhD or MS in a quantitative discipline, e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields, or equivalent research or industry experience, Expected graduation date of December 2025 through August 2026 Solid background in NLP, large language models, speech recognition and modelling, or personalization/recommendation. Familiarity with state-of-the-art practice in these domains Proficient in Python, and experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) Scientific thinking, ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Solid written and spoken communication to effectively communicate technical concepts and results to both technical, and business audiences Curious, hardworking, detail-oriented and motivated by complex analytical problems Ability to work both independently and in highly collaborative team environments

 

Preferred qualifications, capabilities, and skills

Strong background in Mathematics and Statistics Familiarity with the financial services industries Published research in areas of natural language processing, deep learning, or reinforcement learning at a major conference or journal Ability to develop and debug production-quality code Familiarity with continuous integration models and unit test development Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal