Top AI & Machine Learning Jobs in Seattle, WA

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Leads an applied machine learning team and owns fraud detection and identity model strategy, development, deployment, monitoring, and iteration. Partners with Product, Engineering, Risk, and senior leadership to set priorities and deliver business impact. Provides technical mentorship, reviews code, guides modeling and architecture decisions, researches fraud patterns, develops ML capabilities, and shapes responsible AI use under data governance and security constraints.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

Manage and grow a team of data scientists building end-to-end fraud detection models and products. Provide technical mentorship, lead cross-functional planning and communication, develop production-ready models and code, research new fraud types, and deliver data-driven insights to inform product, operations, and business strategy.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Build, train, and productionize machine learning models for fraud detection and identity verification. Perform data acquisition, feature engineering, experimentation, monitoring, and analyses to inform product, sales, and risk decisions. Collaborate with engineering, data acquisitions, and risk operations to maintain data quality and ship production-ready code. Research new fraud types and contribute to new product development across the ML lifecycle.

Build and deploy production ML models for fraud and identity risk across the full ML lifecycle. Research new fraud types, engineer features, write production-ready code, collaborate with cross-functional teams, and inform product, data acquisition, and business decisions.

Design, develop, and productionize machine learning models for fraud detection and identity verification. Perform end-to-end model lifecycle work including data acquisition, feature engineering, training, experimentation, deployment, and monitoring. Research new fraud types, build product-focused models, write production-ready code, and collaborate with engineering, operations, and data teams to support product and business needs.