Top Remote Senior Level Machine Learning Engineer Jobs in Seattle, WA

Develop ML features, models, and analytical methods from device, network, browser, mobile, session, and behavioral telemetry to detect fraud and improve identity confidence. Handle large-scale, noisy, high-cardinality data, design validations, assess drift and leakage, collaborate with engineering/product/risk for production rollout, document models and features, and mentor junior team members.

Develop, implement, and validate production-level control and learning algorithms (MPC, RL, IL) for real-world embodied robotic systems. Design experiments to improve robustness and adaptability, integrate AI control stacks with perception and systems engineering, and use simulation and digital twins to accelerate deployment.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Lead customer-facing engagements to design, build, and productionize end-to-end data and AI solutions on Databricks. Own architecture, implement data pipelines, integrate ML/AI models and user interfaces, drive technical delivery, enable customer adoption, and produce reusable accelerators to scale impact.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

Build zero-to-one AI-native product experiences: prototype, validate with users, and productionize AI agents and workflows. Partner with product, design, research, and engineering to define, evaluate, monitor, and improve ML-driven user-facing systems.

As a Senior Applied AI Engineer, you will design, build, and maintain AI systems using LLMs and backend services, ensuring reliability and performance in production. Responsibilities include developing workflows, monitoring AI behaviors, and collaborating across teams to enhance engineering standards.