AI Agents Applied Research/Engineering Lead - Vice President

Transform how millions of customers manage their money, make decisions, and get more from their financial relationships through a human-centered approach that blends cutting-edge AI with clear, trustworthy experiences.

Chase serves over 80 million customers and is building the next generation of conversational AI to power personalized financial decision-making across travel, banking, lifestyle services, and more.

We're looking for an AI Agents Applied Research/Engineering Lead to drive the research, design, and deployment of agentic AI systems at the heart of that effort.

As AI Agents Applied Research/Engineering Lead, you will work with the team to shape how millions of customers discover, decide, and act-turning multi-step financial tasks into simple conversations.

You'll lead the end-to-end lifecycle of LLM-based agents: defining research directions in areas like multi-step planning, tool use, and safety; building production systems that perform under real-world latency, accuracy, and compliance constraints; and partnering with Product, Engineering, Design, and Risk teams to bring those systems to market.

The problems here are genuinely unusual-building AI that must be not just accurate but auditable, explainable, and safe in a highly regulated, high-stakes domain.

You'll have the opportunity to publish at top-tier venues like NeurIPS, ICML, and ACL-and see that research deployed to a user base of over 80 million customers.
Job Responsibilities


* Lead research and deployment of agentic AI systems with multi-step workflows, tool calling, and multi-agent orchestration.


* Fine-tune and optimize LLMs using parameter-efficient fine-tuning (PEFT), distillation, and quantization to meet production constraints such as latency, memory, and cost.


* Apply reinforcement learning and preference optimization to improve personalization and dialogue policies.


* Scale LLM systems through caching, batching, prompt governance, and evaluation frameworks.


* Implement privacy, safety, and security controls including PCI compliance, jailbreak resistance, and auditability.


* Design rigorous experiments with strong baselines and meaningful metrics.


* Define and track success metrics for agent performance, including task completion rate, accuracy, latency, and customer satisfaction.

Required Qualifications, Capabilities, and Skills


* Ph.D.

with 1+ years or M.S.

with 3+ years building and deploying AI systems in production


* Applied GenAI experience with LLMs including fine-tuning, prompt engineering, and RAG.


* Experience scaling LLM systems with caching, batching, governance, and evaluation.


* Strong foundation in ML, deep learning, statistical modeling, and experimental design.


* Experience in Information Retrieval (indexing, ranking, retrieval) and/or recommendation systems.


* Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn)


* Demonstrated ability to ...