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

JPMorgan Chase & Co.•New York, NY

9d•$135,000 - $155,000•Hybrid

About The Position

As a Summer Associate within JPMorgan Chase’s Machine Learning Center of Excellence (MLCOE), housed in the Chief Data & Analytics Office, you will apply advanced large language model and agentic AI methods to complex problems in areas including natural language processing, question answering, speech recognition and understanding, reinforcement learning, post-training, and recommendation systems. Working with MLCOE mentors, business experts, and technologists, you will own a research project from start to finish and gain hands-on experience addressing real business problems at scale. Project work may have a direct impact on JPMorgan’s businesses, be integrated into product pipelines, or contribute to research published at leading AI conferences. The Summer Associate Internship Program begins in June, depending on the academic calendar. Professional development includes project work related to academic and professional interests, mentorship, and a speaker series with senior leaders. Full-time employment offers may be extended following successful completion of the program. The role operates within the company’s hybrid work model.

Requirements

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 the following: 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 experience with deep learning frameworks such as PyTorch or TensorFlow, the Hugging Face ecosystem, and core data libraries such as NumPy, Pandas, and scikit-learn.
Clear written and spoken communication for technical and business audiences, and ability to work independently and as part of a team.

Nice To Haves

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 building evaluation frameworks for LLM or agent outputs.
Published research in agentic AI, NLP, speech, reinforcement learning, or related areas at a major conference or journal.
Ability to write and debug production-quality code.
Familiarity with the financial services industry.

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 translate business problems into AI solutions and help move them toward production.
Document methodology, assumptions, and results to make work reproducible, and present findings and key learnings to technical and business audiences.