JPMorgan Chase
Applied AI ML Lead - AI Agents & Agentic Systems
$171K–$260K
Palo Alto, CA +3 · Full-time · Posted Oct 13, 2026
- Location
- Palo Alto, CA
Seattle, WA
New York, NY
Jersey City, NJ - Experience
- At least 5 years of applied machine learning experience; at least 5 years applying data science or ML to business problems; at least 5 years with a programming language such as Python, Java, or C/C++.
- Education
- MS and/or PhD in Computer Science, Machine Learning, or a related field.
About the job
Leads the design, experimentation, and production delivery of machine learning and agentic AI systems for business problems, while guiding technical practices and collaborating across teams.
What you'll do
- Evaluate, benchmark, and tune machine learning, LLM, and agentic techniques.
- Develop Python proofs of concept and production-ready solutions, optimizing accuracy and performance.
- Integrate Generative AI into the ML platform and guide product and technical decisions.
What they ask for
- MS and/or PhD in Computer Science, Machine Learning, or a related field.
- At least 5 years of applied machine learning experience and 5 years applying data science or ML to business problems.
- At least 5 years with a programming language such as Python, Java, or C/C++; intermediate Python is required.
- Experience with LLMs and agentic systems, applied ML research and experimentation, and deep learning frameworks such as PyTorch or TensorFlow.
Nice to have
- Experience with distributed training frameworks.
- Experience with cloud ML deployment, such as AWS, Sagemaker, or EKS; advanced knowledge of reinforcement learning or meta learning.
Pay
$171,000.00-$260,000.00 USD base salary (Palo Alto, CA); $171,000.00-$260,000.00 USD base salary (Seattle, WA); $171,000.00-$260,000.00 USD base salary (New York, NY); $171,000.00-$260,000.00 USD base salary (Jersey City, NJ)
Tools
Python, Java, C/C++, PyTorch, TensorFlow, AWS, Sagemaker, EKS
AI focus
Applied machine learning, deep learning, LLMs, Generative AI, and agentic systems, including multi-agent orchestration, reasoning, evaluation, benchmarking, and production integration.
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