Applied AI ML Director

Applied AI ML Director

Palo Alto, CA, United States

Up to $325,000/ year

Artificial Intelligence Engineer AI Researcher Research Scientist MLOps Engineer Python Developer Machine Learning Engineer Staff Engineer Principal Engineer Data Scientist Full Stack Python Developer

JPMorgan Chase

Job Description

Are you passionate about harnessing the power of artificial intelligence and machine learning to solve real-world challenges? At JPMorgan

Chase, we're transforming the way payments work in the Commercial & Investment Bank by leveraging classical and cutting-edge AI/ML technologies.

As an Applied AI ML Director in the Commercial & Investment Bank at JPMorgan

Chase, you'll play a pivotal role in strategizing and building innovative solutions that enhance trust, safety, and operational efficiency for one of the world's leading financial institutions. You will own solutions end-to-end, from problem framing and data strategy to production deployment and measurement. You will remain hands-on while setting technical direction and partnering across product, engineering, data, risk, and compliance stakeholders.

Job Responsibilities

• Demonstrated expertise in several areas from Graph Networks, Neural Networks, NLP, Vision, Classical ML and other technologies
• Domain expertise to develop and improve Trust & Safety problems in payment processing (e.g. Fraud Prevention, Authorization Optimization, Abuse).
• Own end-to-end delivery of problems in Payments (Trust & Safety or otherwise) solutions, from opportunity sizing and requirements through production rollout and iteration.
• Demonstrated ability to envision and develop AI/ML strategy that has platform wide impact within payments Organization.
• Define evaluation strategies and success metrics, including offline validation, error analysis, robustness testing, and controlled online measurement where appropriate.
• Establish model lifecycle practices including reproducibility, testing, monitoring, drift detection, and incident response to sustain reliable production performance.
• Partner with risk and compliance stakeholders to ensure appropriate documentation, controls, explainability expectations, and audit-ready processes.
• Drive technical decisions through design reviews, code and model reviews, and pragmatic standards that raise quality and delivery velocity.
• Communicate tradeoffs and recommendations to senior stakeholders, translating model behavior into decision-ready business impact.

Required Qualifications, Capabilities, and Skills

• PhD in applied artificial intelligence, machine learning concepts or similar with 5+ years of experience or MS in applied artificial intelligence, machine learning concepts or similar with 8+ years experience.
• Experience building and delivering applied machine learning or natural language processing solutions with measurable outcomes in production.
• Strong programming skills in Python and experience using modern machine learning frameworks such as PyTorch or Tensor Flow.
• Hands-on experience with document extraction and natural language processing techniques including text classification and information extraction.
• Experience designing data-driven solutions using SQL and distributed processing tools such as Spark or equivalent.
• Experience deploying and operating machine learning services or pipelines in a cloud environment such as Amazon Web Services (or equivalent).
• Demonstrated ability to translate ambiguous business problems into structured machine learning plans, including data strategy, evaluation, rollout, and operationalization.
• Strong communication and collaboration skills, including the ability to explain technical tradeoffs to technical and non-technical partners.

Preferred Qualifications, Capabilities, and Skills

• Experience with optical character recognition and document understanding workflows for scanned or semi-structured documents.
• Experience with modern natural language processing architectures such as transformer-based models and techniques for optimization and efficient inference.
• Experience with machine learning operations practices and tooling, including model registries, continuous integration and delivery for machine learning, and…