Applied AI/ML Lead - Payments

JPMorgan Chase & Co.•Seattle, WA

20h•$164,350 - $260,000

About The Position

Are you passionate about harnessing the power of artificial intelligence and machine learning to solve real-world challenges? At JPMorganChase, we’re transforming the way payments work in the Commercial & Investment Bank by leveraging cutting-edge document extraction and natural language processing (NLP) technologies. As a Vice President and Applied AI/ML Lead, you’ll play a pivotal role in building innovative solutions that enhance trust, safety, and operational efficiency for one of the world’s leading financial institutions. As a Vice President, Applied AI and Machine Learning Lead at JPMorganChase within Payments Technology in the Commercial & Investment Bank, you will lead the delivery of document extraction and natural language processing capabilities that improve trust, safety, and operational effectiveness. 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.

Requirements

Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
5+ years of 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 TensorFlow.
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.

Nice To Haves

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 observability.
Experience with real-time or event-driven architectures supporting low-latency inference and feature generation.
Experience applying document extraction or natural language processing in payments, financial services, or regulated environments.

Responsibilities

Own end-to-end delivery of document extraction and natural language processing solutions, from opportunity sizing and requirements through production rollout and iteration.
Design scalable model pipelines for document ingestion, text extraction, classification, and ranking, balancing accuracy, latency, throughput, and cost.
Develop and improve natural language processing algorithms and model approaches to extract entities, relationships, and signals from unstructured text and documents.
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.