NLP Summer Associate

About the Team

JPMorgan Chase is applying AI at a global scale and invests over $18 billion annually in technology, including innovation, data, and security. The Machine Learning Center of Excellence (MLCOE) sits within The Chief Data & Analytics Office (CDAO). It builds and deploys AI and machine learning solutions for complex business challenges across the firm's flagship AI deployments, supporting financial products, productivity, and risk management.

As a Summer Associate within MLCOE, you will apply advanced LLM and agentic AI methods to problems in natural language processing, question answering, speech recognition and understanding, reinforcement learning, post-training, and recommendation systems. You will work alongside MLCOE mentors, business experts, and technologists, and own a research project from start to finish. More information is available at jpmorgan.com/mlcoe.

The Program

The Summer Associate Internship Program begins in June, depending on your academic calendar. Support includes project work tied to your academic and professional interests, mentorship, and a speaker series with senior leaders. Projects may have a direct impact on JPMorgan's businesses, be integrated into product pipelines, or become published research at top AI conferences. Full-time employment offers may be extended after successful completion of the program. The role follows a hybrid work model.

Job Responsibilities

• Perform exploratory data analysis, feature engineering, model development, and evaluation on structured and unstructured datasets.
• Design and build prototypes of agentic AI systems, including multi-step workflows that use tools and reason across tasks.
• Apply and experiment with LLM techniques such as retrieval-augmented generation, tool and function calling, fine-tuning, and agent harness design.
• Develop evaluation frameworks to measure the quality, reliability, and safety of LLM and agent outputs.
• Work with mentors, business stakeholders, and engineers to turn business problems into AI solutions and help move them toward production.
• Document methodology, assumptions, and results so work can be reproduced, and present findings to technical and business audiences.