AI Research Associate Program - Internship

J.P. Morgan's AI Research team is a global group of scientists, engineers, and product managers focused on developing new AI capabilities and implementing them across the firm. They work on areas such as multi-agent systems, foundation model training, reinforcement learning, multimodal document AI, formal reasoning, planning, verification, and synthetic data generation, with a focus on trustworthy AI, privacy, and cryptography for financial services.

AI Research is part of the Chief Data & Analytics Office (CDAO), which drives the firm's data and analytics initiatives, ensuring data quality, security, and leveraging AI/ML for new products, productivity improvements, and risk management.

What You'll Do

As a Research Associate, you will:

• Collaborate on commercially focused research projects with internal data scientists, engineers, and stakeholders across various business areas (e.g., Commercial & Investment Banking, Asset & Wealth Management, Consumer & Community Banking).
• Formulate problems, develop new algorithms and models, conduct experiments, evaluate results, and deliver high-quality, tested Python code.
• Contribute to business applications, reusable assets, products, and research initiatives.
• Share knowledge through white papers, publications, and presentations.

What You'll Need

Required Qualifications:

• Currently pursuing a PhD in Computer Science, Engineering, or a related field with relevant AI/ML research experience.
• Research publications in top-tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, ICAPS, CRYPTO).
• Ability to write modular, optimized, high-quality Python code with tests.
• Deep understanding of fundamental AI/ML techniques and current state-of-the-art in your area of expertise.
• Practical experience with statistical data analysis, experimental design, and evaluation metrics.
• Effective verbal and written communication skills, able to address both technical and business audiences.
• Motivation to impact problems in financial services.
• Curiosity, creativity, resourcefulness, and a collaborative spirit.
• Strong research track record in one or more of the following areas:
  • Synthetic Data Generation (tabular, images, time-series, agent simulations)
  • Hybrid agent architectures (LLMs, AI planning, formal logic, neuro-symbolic approaches)
  • Multimodal information discovery (websites, news, documents, structured data, images, audio)
  • Foundation model training for structured data (tabular, graph, time-series)
  • Reinforcement Learning and continual learning for agents
  • Advanced multi-agent systems (coordination, negotiation, mechanism design)
  • Cryptography and secure multi-party computation for privacy-preserving ML.

Preferred Qualifications:

• Experience with ML libraries (e.g., PyTorch, TensorFlow, HuggingFace) and agent frameworks (e.g., LangGraph).
• Familiarity with formal verification tools and languages (e.g., Coq, TLA+, Z3).
• Practical software development experience in collaborative settings (e.g., open-source projects).
• Proven ability to translate business requirements into technical solutions.