Applied AI ML Researcher Lead

JPMorgan Chase & Co.•Palo Alto, CA

3h•$164,350 - $260,000

About The Position

Our goal is to build the next generation of AI: autonomous agents that can reason, plan, act, and learn to solve critical problems for an industry leading financial institution. We are looking for architects who will define the future of banking through Agentic AI. The Applied Artificial Intelligence and Machine Learning team in Commercial and Investment Banking is transforming operations by leveraging the latest advancements in agentic AI and frontier models. As an Applied Artificial Intelligence and Machine Learning Lead at JPMorganChase within the Applied AI Research team in Commercial and Investment Banking, you will help build autonomous agent systems that can reason, plan, act, and learn to solve high-impact business problems. You will bridge cutting-edge research and enterprise-grade delivery, shaping how applied AI is designed, evaluated, and deployed at scale. You will partner closely with business and technology stakeholders to translate priority needs into measurable outcomes, while raising engineering and scientific standards across the team.

Requirements

Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
Advanced degree (Master’s or Doctorate) in Computer Science, Machine Learning, Statistics, or a related quantitative field
Demonstrated experience designing experiments and evaluations for machine learning systems, including clear metrics and reproducible results
Proven track record deploying machine learning applications into production environments with operational excellence (monitoring, reliability, and lifecycle management)
Strong software engineering skills in modern programming languages and machine learning frameworks, with experience building reusable components
Experience with distributed computing patterns for training and serving, and with state management for agent workflows
Ability to lead through influence across cross-functional teams, translating complex technical topics for varied audiences

Nice To Haves

Experience building and evaluating autonomous agent systems (planning, tool-use, orchestration, and multi-agent coordination)
Experience with cloud-based machine learning platforms, such as Amazon Web Services (AWS) SageMaker or Amazon Bedrock
Publications, open-source contributions, or other evidence of applied research impact in machine learning or generative artificial intelligence
Familiarity with financial services domains and operational processes, including risk-aware design and production constraints

Responsibilities

Architect and deliver generative artificial intelligence and agent-based solutions that automate complex operational workflows end-to-end
Translate priority business problems into research and engineering plans, success metrics, and scalable solution designs
Build multi-agent systems that collaborate reliably, coordinate tasks, and improve over time through feedback and evaluation
Design reusable services, libraries, and reference architectures that accelerate adoption across applied AI teams
Establish rigorous experimentation practices, including offline/online evaluation, ablation testing, and error analysis, to drive measurable improvements
Mentor and develop applied AI engineers and researchers, fostering a culture of scientific rigor, ownership, and continuous learning
Partner with stakeholders across business and technology teams to scale solutions responsibly, balancing performance, reliability, and security