Join JPMorgan Chase's Chief Data & Analytics Office (CDAO) and be part of a mission to modernize compliance through scalable and explainable AI. As a Machine Learning Engineer, you'll design and build systems that answer critical data usage questions with prediction, logic, proof, and intelligent automation. Work at the intersection of applied machine learning, AI reasoning systems, and data governance to tackle complex problems and build ML solutions that make decisions.

As a Machine Learning Engineer within JPMorgan Chase's Chief Data & Analytics Office (CDAO), you will design and build systems that answer critical data usage questions with prediction, logic, proof, and intelligent automation. You will work at the intersection of applied machine learning, AI reasoning systems, and data governance to tackle complex problems and build ML solutions that make decisions. You will build and integrate ML models into structured backend services, write production-ready Python code, and assist in building automated workflows. You will collaborate with VP engineers and cross-functional partners to understand requirements and execute implementation. Your role involves participating in code reviews, quality assurance, and ongoing system improvement.

 

Job Responsibilities:

Build and integrate ML models into structured backend services (APIs, pipelines, batch processors).Write production-ready Python code to support model inference, validation, and logging.Assist in building automated workflows for data ingestion, model deployment, and metadata tagging.Build dashboards, logs, or simple UI tools to visualize and debug decision outcomes.Collaborate with VP engineers and cross-functional partners to understand requirements and execute implementation.Participate in code reviews, quality assurance, and ongoing system improvement.

 

Required Qualifications, Capabilities, and Skills:

Bachelor’s degree in Computer Science, Software Engineering, or related field.Commercial software development experience, ideally with exposure to ML/AI systems.Strong programming skills in Python; familiarity with web frameworks (Flask, FastAPI).Understanding of model inference lifecycles, APIs, and data validation.Familiarity with Git, CI/CD pipelines, testing, and performance profiling.Ability to work independently and deliver clean, maintainable, production-quality code.

 

Preferred Qualifications, Capabilities, and Skills:

Master’s degree or certifications in ML engineering, MLOps, or cloud infrastructure.Familiarity with data cataloging, tagging, or schema inference workflows.Exposure to enterprise governance, compliance, or secure access systems.Interest in explainable AI, decision support tooling, and intelligent policy engines.