Applied Artificial Intelligence Machine Learning Lead Vice President

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 127 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $207k

$152k most similar roles pay here $256k

This listing doesn't post a salary. Most similar roles pay $168,225–$246,150.

Based on 239 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied Artificial Intelligence Machine Learning Lead Vice President

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As an Applied Artificial Intelligence/ Machine Learning Lead - Vice President within Global Private Bank, you will lead the design and construction of agentic AI systems that execute reliable business workflows end-to-end. You will build advanced solutions across NLP, speech analytics, time series, reinforcement learning, and recommendation systems while developing robust architectures involving memory, state, context management, and loop engineering. Your daily work involves creating knowledge-centric reasoning layers using knowledge graphs and hybrid retrieval, as well as implementing specification-driven development through schemas and evaluation harnesses. You will utilize Python, PyTorch, and TensorFlow to develop tools for automated critique loops and prompt optimization. The role focuses on solving complex problems in agent orchestration, ensuring traceability and control-aware behavior while translating research in LLMs and self-improving systems into practical capabilities within a highly collaborative technical environment.

Skills

LLMs Agentic AI RAG Knowledge Graphs Python PyTorch TensorFlow Reinforcement Learning NLP Speech Analytics Time Series Recommendation Systems Data Science

What you'll do

Design and build agentic AI systems that execute reliable end-to-end business workflows.
Develop robust agent architectures incorporating memory, state management, tool orchestration, and loop engineering.
Engineer reliable workflows with high standards for correctness, traceability, and control-aware guardrails.
Build knowledge-centric reasoning layers using knowledge graphs and hybrid retrieval methods to improve accuracy.
Drive specification-driven development by authoring technical schemas, contracts, and evaluation harnesses.
Improve agent quality through recursive self-improvement, automated critique loops, and red-team feedback.
Mentor team members while maintaining high standards for engineering rigor and research depth.

What we're looking for

PhD in a quantitative discipline or equivalent experience (3+ years with PhD depth or 5+ years with an MS).
Expertise in building agentic AI systems including memory, state, context management, and tool orchestration.
Experience in loop engineering, specification-driven development, and prompt/skill instruction optimization.
Proficiency in ML/DL methods and tools such as PyTorch, TensorFlow, and the core Python data stack.
Ability to design experiments and evaluation frameworks aligned with business outcomes like quality, reliability, and safety.
Experience with scalable data and model workflows for training and inference using strong software engineering practices.
Strong communication skills to explain technical concepts to both technical and business audiences.
Knowledge of advanced techniques including reinforcement learning, knowledge graphs, and RAG systems.

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