Drimva

JPMorgan Chase

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

$135K–$155K

New York, NY +2 · Full-time · Posted Oct 20, 2026

Location
New York, NY
Seattle, WA
Palo Alto, CA
Education
Currently enrolled in a PhD or MS program in Computer Science, Electrical Engineering, Mathematics, Operations Research, Data Science, or a related quantitative field.

About the job

Conduct an end-to-end research project applying LLM and agentic AI methods to business problems, from data analysis and prototyping through evaluation and presentation.

What you'll do

  • Analyze data, engineer features, and develop and evaluate models.
  • Build agentic AI prototypes and evaluation frameworks for LLM and agent outputs.
  • Collaborate on AI solutions, document and present findings, and help advance work toward production.

What they ask for

  • Currently enrolled in a PhD or MS program in a quantitative field, with expected graduation between December 2027 and August 2028.
  • Background in at least one of LLMs and agentic AI, NLP, speech recognition, or personalization/recommendation; familiarity with current methods.
  • Strong foundations in statistics, probability, and machine learning, including experimental design and metric definition.
  • Hands-on experience with modern LLM application patterns; strong Python skills, including deep learning frameworks, Hugging Face, and core data libraries.

Nice to have

  • Experience building LLM prototypes with tools, APIs, documents, databases, or search systems; experience with fine-tuning/post-training or LLM evaluation.
  • Relevant publications, production-quality coding ability, or familiarity with financial services.

Pay

$135,000.00-$155,000.00 USD base salary (New York, NY); $135,000.00-$155,000.00 USD base salary (Seattle, WA); $135,000.00-$155,000.00 USD base salary (Palo Alto, CA)

Tools

Python, PyTorch, TensorFlow, Hugging Face, NumPy, Pandas, scikit-learn, LangGraph

AI focus

Applies advanced LLM and agentic AI methods to NLP, question answering, speech, reinforcement learning, post-training, and recommendation; builds tool-using workflows and evaluates their quality, reliability, and safety.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 6, 2026 (UTC)