Applied AI/ML Lead - Payments
On-siteSeattle, Washington, United States
Job Summary
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 to extract entities, relationships, and signals from unstructured text, while defining evaluation strategies including offline validation, error analysis, and controlled online measurement. Establish model lifecycle practices for reproducibility, testing, monitoring, and drift detection 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 and pragmatic standards that raise quality and delivery velocity, communicating tradeoffs to senior stakeholders.
Required Qualifications
- Formal training or certification on applied artificial intelligence and machine learning concepts
- 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
- 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
Desired Qualifications
- 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
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