Top Senior Machine Learning Jobs in Seattle, WA

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

Design and deploy scalable machine learning solutions for personalization, recommendation, ranking, retrieval, and traveler understanding. Lead end-to-end scientific work spanning data exploration, feature engineering, model development, experimentation, evaluation, and productionization. Partner with engineering, product, and business teams on system and API integration, improve model reliability and operational quality, influence technical direction, mentor scientists, and apply advanced methods including neural recommenders, foundation models, LLMs, and representation learning.

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

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Drive end-to-end ML solutions for search, ranking, recommendations, and personalization. Build production model pipelines, scalable data pipelines, monitoring, A/B testing, and collaborate with cross-functional teams to translate business needs into measurable, reliable AI systems.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Lead development and production deployment of ML and deep-learning models for sponsored ads selection, relevance, ranking, CTR prediction, auction dynamics, and campaign optimization. Collaborate with product and engineering, run experiments, mentor junior scientists, deploy distributed pipelines at scale, communicate results to senior leadership, and publish research in ML/advertising venues.

Design, implement, and productionize scalable ML models and microservices; build and maintain data pipelines; prototype solutions, participate in architecture and design reviews, and collaborate with cross-functional teams to deliver ML-powered product features.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead full-stack development and technical strategy for Copilot Discover: design, build, and operate scalable ML/deep-learning models and prompt strategies; implement responsive TypeScript/React/NextJS/Web Components front-ends; develop REST APIs and data layers; ensure performance, scalability, and reliability; collaborate across UX, backend, and ML teams; participate in code reviews and architecture decisions.

Lead the development of machine learning solutions for video personalization, enhancing user experience through data-driven insights and collaboration across teams. Drive technical strategy in building scalable systems and partnerships to innovate customer recommendations.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Lead end-to-end applied AI/ML solutions for payments trust & safety, from problem framing and data strategy through production deployment, monitoring, and iteration. Drive technical direction, evaluation metrics, reproducibility, and compliance-ready documentation while partnering across product, engineering, data, risk, and compliance stakeholders.

Lead research and development of auction mechanism design for online advertising. Evaluate and improve ranking/pricing algorithms, run large-scale experiments (A/B), build data pipelines and production software, apply ML/RL and optimization, and collaborate cross-functionally to deliver measurable business impact.

Design, build, and operate scalable ML systems and services end-to-end: data sourcing, feature engineering, model development, deployment, monitoring, and operational support. Collaborate across product, data, and engineering, drive ML best practices, and mentor other engineers to improve model reliability, performance, and observability.

Lead a team developing ranking, recommendation, and intent models for bundled travel products. Define ML roadmaps, oversee model design, evaluation, A/B testing, causal inference, and productionization. Partner with engineering and product to deploy scalable, observable systems, and mentor ML scientists to raise technical rigor and impact.

Design, build, and scale production ML systems for distribution and supply; own end-to-end ML feature delivery from data and modeling to deployment and monitoring; collaborate with cross-functional teams; improve observability, reliability, and operational excellence; mentor engineers and apply advanced ML tooling and platform practices.

Design, implement, and deploy end-to-end machine learning solutions to improve post-booking customer experience. Build and validate models, run experiments (A/B), measure business impact, and collaborate with product, engineering, and stakeholders to productionize and monitor ML systems.