What are the responsibilities and job description for the Applied AI ML Researcher Director position at JPMorgan Chase?
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 AI ML Researcher Director in the Applied AI Research team, you will be a builder, a scientific pioneer and mentor who bridges the gap between leading edge theory and enterprise-grade, large-scale, deployable systems.
Job Responsibilities:
- Architect and develop GenAI and agentic solutions to automate complex operational processes
- Assist Lines of Business and teams to directly solve priority use cases within the domain
- Deliver multiple agents that collaborate to solve large, complex problems, orchestrate end-to-end workflows, and scale across JPMC
- Design and build services and libraries that AI teams want to use
- Mentor and inspire a team of AI engineers, fostering a culture of excellence, innovation, and continuous learning
- Ensure the scalability, reliability, and security of AI/ML solutions in a production environment, with a focus on long-term sustainability
- Collaborate with stakeholders and technical partners across Lines of Business and firmwide to scale solutions and maximize impact
Required Qualifications, Capabilities, and Skills:
- PhD in Computer Science or a related quantitative discipline with 8 years of relevant experience or MS in Computer Science or a related field with 12 years of relevant experience
- Research experience or work in a top commercial AI research lab
- Deep understanding of AI fundamentals and practical experience with data analysis and experimental design
- Proven track record of deploying AI/ML applications in a production environment at scale
- Familiarity with distributed computing patterns for training, serving, and persistence of state
- Experience integrating user feedback to establish agentic refinement and self-improving AI applications
- Experience in building and leading high-performing AI teams
Preferred Qualifications, Capabilities, and Skills:
- Experience deploying models on AWS platforms such as SageMaker or Bedrock will be strongly considered, but not required
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