Top Fintech Machine Learning Engineer Jobs in Seattle, WA

As a Senior Machine Learning Engineer, you will build scalable ML systems, lead the ML lifecycle, mentor engineers, and collaborate across teams.

The role involves designing and implementing ML systems for pricing, managing data pipelines, and improving model performance and reliability. It requires collaboration with cross-functional teams and mentorship for engineers.

As a Staff AI Software Engineer, you'll develop and optimize scalable AI applications, manage deployment pipelines, and collaborate with cross-functional teams to drive innovative solutions.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

As a Senior Engineering Manager for ML & AI, you'll lead Data Science and ML initiatives, mentoring team members, and collaborating across R&D to drive strategic AI solutions that impact the business.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead end-to-end data science projects: build, deploy, and monitor ML models (credit risk, pricing, collections, fraud). Partner cross-functionally, productionize pipelines, write production-grade code, and communicate results to technical and non-technical stakeholders.

The Senior Machine Learning Platform Engineer will build foundational components for ML models, mentor junior engineers, and optimize streaming pipelines. Responsibilities include enhancing ML infrastructure and ensuring data quality.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Senior Machine Learning Engineer will develop and optimize production machine learning models, implementing MLOps practices, monitoring performance, and collaborating across teams.