Artificial Intelligence Research Lead - Vice President

About the role

JPMorganChase AI Research is a global team dedicated to developing novel AI capabilities and translating breakthrough techniques into deployed solutions. The team focuses on areas like multi-agent systems, foundation model training, reinforcement learning, multimodal document AI, formal reasoning, planning, and verification, among others.

Responsibilities

• Work on multiple commercially oriented research projects in collaboration with internal data scientists, applied engineering teams, and stakeholders across various businesses.
• Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments and rigorous evaluations, synthesize and communicate results, and deliver well-tested, high-quality code.
• Contribute to high-impact business applications, reusable assets, and products, and research initiatives.
• Provide thought leadership on internal and external forums through white papers, publications, and presentations.
• Lead projects or major workstreams in larger projects/initiatives. Play a key role in ensuring that problem definitions and solutions are technically sound, generalizable, and capture business/product requirements.
• Proactively identify and propose new research projects linked to business problems.

Requirements

• PhD in Computer Science, Engineering, or related fields with relevant research experience in AI/ML and 2+ years of relevant work experience.
• Research publications in top-tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, ICAPS, CRYPTO).
• Deep understanding of fundamental AI/ML techniques and a strong grasp of current state of the art in specific areas of expertise.
• Practical experience with statistical data analysis, experimental design, and creation of meaningful benchmarks and metrics for evaluation.
• Effective verbal and written communication skills with the ability to address both technical and business audiences.
• Practical software development experience in collaborative project settings such as open-source projects or industry experience. Practical experience with ML libraries (e.g., PyTorch, TensorFlow/Keras, HuggingFace Transformers) and agent frameworks (e.g., LangGraph, Google ADK).
• Proven ability to translate business requirements into technical problem formations and deliver novel solutions into production.

Qualifications

• Curiosity, creativity, resourcefulness, and a collaborative spirit.
• Familiarity with common formal verification tools and languages (e.g., Coq/Isabelle/Lean, TLA+, Alloy, Z3, PDDL).
• Experience with foundation model training for tabular, time-series, language, and other modalities.
• Experience with multimodal information discovery, understanding, and predictive modeling spanning multiple modalities (e.g., websites, news, audio, speech, documents, images, etc.).