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

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.

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

Lead design and development of AI/ML framework and inference software (ONNX, ONNX Runtime, Foundry Local). Provide technical leadership, mentor engineers, define requirements and design docs, drive performance, reliability, and observability improvements, act as DRI and participate in on-call operations, and collaborate across products to deliver scalable AI solutions.

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.

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.

Build infrastructure and AI systems for Hardware Intelligence: agent infrastructure, retrieval and evaluation pipelines, and tooling to reason over large-scale time series, telemetry, and engineering datasets. Prototype and ship AI-powered workflows for anomaly investigation and root cause analysis, work directly with customers, and establish engineering patterns as the platform scales.

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.

Design and maintain a skill and concept graph, build ML models to infer learner skills, and develop recommendation systems and LLM-driven features. Ship production-ready LLM systems, define explainability standards, partner with frontend and product teams, and monitor model performance and evaluation frameworks.

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.