Applied AI ML Director
Salary not listed • Seattle, WA, United States • Full-time
Posted 1mo ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
JPMorganChase is seeking an Applied AI ML Director to join their Commercial & Investment Bank team in Seattle, WA. This role focuses on transforming payments through classical and cutting-edge AI/ML technologies. You will be responsible for strategizing and building innovative solutions to enhance trust, safety, and operational efficiency, owning projects end-to-end from problem framing and data strategy to production deployment and measurement. While setting technical direction, you will remain hands-on and collaborate with product, engineering, data, risk, and compliance stakeholders.
Responsibilities
- Develop and improve Trust & Safety solutions in payment processing using expertise in Graph Networks, Neural Networks, NLP, Vision, or Classical ML.
- Own the end-to-end delivery of payment solutions, from opportunity sizing and requirements to production rollout and iteration.
- Envision and develop AI/ML strategies with platform-wide impact within the payments organization.
- Define evaluation strategies and success metrics, including offline validation, error analysis, robustness testing, and online measurement.
- Establish model lifecycle practices for reliable production performance, including reproducibility, testing, monitoring, drift detection, and incident response.
- Partner with risk and compliance stakeholders to ensure appropriate documentation, controls, explainability, and audit-ready processes.
- Drive technical decisions through design reviews, code and model reviews, and pragmatic standards.
- Communicate tradeoffs and recommendations to senior stakeholders, translating model behavior into business impact.
Requirements
- PhD in applied AI/ML or similar with 5+ years of experience, or MS with 8+ years of experience.
- Experience building and delivering applied ML or NLP solutions with measurable production outcomes.
- Strong Python programming skills and experience with ML frameworks like PyTorch or TensorFlow.
- Hands-on experience with document extraction and NLP techniques (text classification, information extraction).
- Experience designing data-driven solutions using SQL and distributed processing tools (e.g., Spark).
- Experience deploying and operating ML services or pipelines in a cloud environment (e.g., AWS).
- Ability to translate ambiguous business problems into structured ML plans (data strategy, evaluation, rollout, operationalization).
- Strong communication and collaboration skills, with the ability to explain technical tradeoffs to diverse audiences.