Principal Research Engineer - AI/ML

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 a Applied AI Machine Learning Director at JPMorganChase within the Applied AI Research team in the Commercial & Investment Bank, you will lead the design and delivery of agent-based and generative artificial intelligence solutions that transform complex operations. You will bridge state-of-the-art research and enterprise-grade engineering to build systems that are safe, reliable, and scalable. You will partner across product, engineering, and business teams to prioritize high-impact problems and deliver outcomes. You will also raise the technical bar through mentorship, technical leadership, and strong scientific rigor.
Job responsibilities
Architect end-to-end agent-based and generative artificial intelligence solutions to automate complex operational workflows
Translate ambiguous business problems into research hypotheses, measurable success metrics, and production-ready designs
Build and ship multiple collaborating agents that coordinate planning and execution across large, multi-step processes
Design reusable services, libraries, and evaluation frameworks that accelerate adoption across artificial intelligence and engineering teams
Establish robust experimentation practices, including offline/online evaluation, monitoring, and iterative improvement loops
Partner with stakeholders across teams to identify priority use cases, define roadmaps, and deliver scalable capabilities
Ensure solutions meet enterprise expectations for reliability, security, and long-term maintainability in production
Mentor and coach engineers and researchers through design reviews, technical guidance, and knowledge sharing
Required qualifications, capabilities and skills
Formal training or certification on applied artificial intelligence and machine learning concepts and 10+ years applied experience
Advanced degree (master’s or doctorate) in computer science, engineering, statistics, or a related quantitative discipline, or equivalent practical experience
Demonstrated experience deploying machine learning and/or generative artificial intelligence systems into production at enterprise scale
Strong foundation in machine learning fundamentals, experimental design, and data-driven decision-making
Experience designing distributed systems for model training, inference, and stateful services in production environments
Proven ability to create evaluation strategies for agent-based systems (quality, safety, latency, cost, and reliability), and improve them over time
Strong programming and engineering skills with a track record of building maintainable, reusable components used by other teams
Demonstrated ability to lead through influence in cross-functional environments and drive alignment across technical and non-technical stakeholders
Preferred qualifications, capabilities and skills
Experience deploying and operating machine learning workloads on Amazon Web Services (for example, Amazon SageMaker or Amazon Bedrock)
Publication history, open-source contributions, or demonstrated applied research impact in areas such as large language models, reinforcement learning, or autonomous agents
Experience with containerized and cloud-native deployment patterns (for example, Kubernetes-based platforms)
Familiarity with governance and risk considerations for artificial intelligence systems, including privacy, model safety, and responsible use
Domain experience applying advanced analytics or artificial intelligence to large-scale operational processes in financial services or other regulated industries
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