Machine Learning Center of Excellence (NLP) - Internship

JPMorgan Chase is leveraging AI to transform its global operations, investing heavily in technology and innovation. The Machine Learning Center of Excellence (MLCOE), within the Chief Data & Analytics Office (CDAO), develops and deploys AI and machine learning solutions for complex business challenges.

What You'll Do

As a Summer Associate Intern, you will:

• Apply advanced LLM and agentic AI methods to problems in natural language processing, question answering, speech recognition, reinforcement learning, and recommendation systems.
• Own a research project from start to finish, working alongside mentors, business experts, and technologists.
• Gain hands-on experience solving real business problems at scale.
• Learn from experienced AI researchers and engineers.
• Contribute to projects that may be integrated into product pipelines or presented at AI conferences.

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, and present findings and key learnings to technical and business audiences.

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: 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

• 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.

Key Dates

• The Summer Associate Internship Program begins in June, depending on your academic calendar.