2027 Machine Learning Center of Excellence (NLP) - Summer Associate

Location: New York, NY, United States Department: Data & Analytics

At JPMorgan Chase, AI is transforming how we operate on a global scale. We invest over $18 billion annually in technology, including innovation, data, and security, to shape the future for our clients, communities, and employees. Housed within The Chief Data & Analytics Office (CDAO), the Machine Learning Center of Excellence (MLCOE) creates and deploys AI and machine learning solutions for complex business challenges across flagship AI deployments at the firm. Our solutions power financial products, boost productivity, and strengthen risk management across the firm.

As a Summer Associate within MLCOE, you will apply advanced LLM and agentic AI methods to complex problems in areas such as natural language processing, question answering, speech recognition and understanding, reinforcement learning, post-training and recommendation systems. Working alongside MLCOE mentors, business experts, and technologists, you will own a research project from start to finish. This role offers hands-on experience solving real business problems at scale while learning from experienced AI researchers and engineers. Learn more about MLCOE 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 a direct impact on JPMorgan’s businesses, will be integrated into our product pipelines, or be part of published research in top AI conferences. Full-time employment offers may be extended upon successful completion of the program within our hybrid work model.

Job responsibilities

Perform exploratory data analysis, feature engineering, model development, and evaluation using 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

Collaborate 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 and key learnings to technical and business audiences

Required qualifications, capabilities, and skills

Currently enrolled in a PhD or MS program in Computer Science, Electrical Engineering, Mathematics, Operations Research, Data Science, or a related quantitative field, with an expected graduation date between December 2027 and August 2028

Solid background in at least one of: large language models and agentic AI, natural language processing, speech recognition, or personalization/recommendation, with familiarity with current state-of-the-art methods

Strong foundation in statistics, probability, and machine learning, including the ability to design experiments and define metrics that reflect business goals

Hands-on exposure to modern LLM application patterns such as prompting, embeddings, retrieval-augmented generation, tool/function calling, structured outputs, and multi-step or agentic workflows

Strong Python skills, including deep learning frameworks (PyTorch or TensorFlow), the Hugging Face ecosystem, and core data libraries (NumPy, Pandas, scikit-learn)

Clear written and spoken communication for both technical and business audiences, and the ability to work both independently and on a team

Preferred qualifications, capabilities, and skills

Experience building LLM-powered prototypes that connect models to tools, APIs, documents, databases, or search systems, using frameworks such as LangGraph, Google ADK, LangChain, LlamaIndex, or Semantic Kernel

Experience with fine-tuning or post-training (e.g., TRL) and with building evaluation frameworks for LLM or agent outputs

Published research in agentic AI, NLP, speech, reinforcement learning, etc at a major conference or journal

Ability to write and debug production-quality code

Familiarity with the financial services industry

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