Top Machine Learning Jobs in Seattle, WA

The Applied Scientist will tackle various machine learning problems, enhance valuation systems, and deploy deep learning models while collaborating with cross-functional teams.

The Machine Learning Engineer will develop and improve ML models for product surfaces, build data processing pipelines, and leverage machine learning technologies in advertisements.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

Lead and deliver the agentic AI roadmap for Azure Supportability. Align cross-functional teams, define requirements and OKRs, quantify AI impact, conduct research and competitive analysis, manage stakeholders, and address technical challenges to improve customer experience and operational efficiency.

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 deploy large-scale ML models powering core products; own full ML lifecycle from data analysis to production; partner cross-functionally to prototype and launch features; apply modern ML techniques and AI tooling while ensuring code quality, security, and scalable production services.

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 presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

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

Develop and test AI/ML software solutions in collaboration with engineering, product, and program teams. Apply engineering principles, responsible AI practices, and emerging machine learning techniques to build reliable, efficient, observable products. Responsibilities include understanding user requirements, learning new technologies, improving technical solutions through feedback, and completing software projects in a collaborative environment.

Leads customer-facing AI pre-sales engagements and designs tailored enterprise AI architectures. Advises executive stakeholders on AI strategy, ROI, governance, secure LLM integration, RAG, cloud deployment, networking, datacenter infrastructure, and conversational or agentic AI. Partners with sales, engineering, and technical domain teams to evolve AI offerings, guide digital transformation, and promote scalable, ethical, high-performance deployments.

Lead and translate AI research into developer products: build datasets from public and internal code, design and run large-scale ML experiments, train state-of-the-art models, collaborate with product teams across Microsoft and GitHub, and evaluate and iterate AI systems for software engineering.

Lead and grow an engineering team building SDKs, UIs, and agentic developer experiences for Generative AI. Set technical/product direction, coach engineers, ensure production reliability, convert customer feedback into platform features, and drive AI-first engineering practices and incident readiness.

Lead architecture and specification of high-performance AI accelerators. Build functional and analytical models, collaborate with hardware and software teams, and guide design, verification, emulation, firmware, and kernel teams through end-to-end silicon development to optimize performance, power, cost, and time-to-market for cloud-scale AI workloads.

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 document extraction and NLP solutions for payments: design scalable model pipelines, develop extraction/classification models, define evaluation and lifecycle practices, ensure compliance and explainability, and partner with cross-functional stakeholders to productionize and monitor ML systems.

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.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

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

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

Enterprise hunter role selling AI data solutions (data collection, annotation, LLM fine-tuning, generative AI) to Fortune 500 clients. Build pipeline, close high-value accounts, act as consultative SME on AI data preparation and workflows, and partner with solutions architects and PMs to ensure successful delivery while exceeding revenue targets.