Top Machine Learning Jobs in Seattle, WA

Develops scalable automation, AI, machine-learning, and data-analysis solutions for quantum-device research. Responsibilities include building experimental data pipelines, applying AI to device characterization and tuning, integrating models into simulation and control environments, creating reproducible workflows, and communicating technical solutions. The role collaborates across quantum research, engineering, software, and AI disciplines, with opportunities to build AI agents and copilots for experiment setup, log triage, reporting, and knowledge retrieval.

Lead the end-to-end development and deployment of advanced machine learning models for marketplace risk and ecommerce challenges. Responsibilities include data-source validation, exploratory analysis, feature engineering, model evaluation, scalable ML architecture, stakeholder consultation, and translating business needs into measurable data strategies. The role also requires mentoring technical team members, applying emerging AI techniques, and driving reliable, high-impact data science solutions.

Build and operate large-scale ML infrastructure, including feature pipelines, model training and evaluation systems, high-performance inference services, data management platforms, and model orchestration tools. The role partners with ML engineers to deploy models into production, improve system reliability and efficiency, and maintain secure, scalable, production-ready services using Python, Java, and related AI technologies.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Design, develop, and operate scalable VoIP infrastructure and telephony platforms supporting AI-driven voice workloads. Integrate SIP/RTP systems, LLMs, and real-time communications while improving observability, reliability, deployment, and SRE practices. Build tested, reusable software across telephony and AI integration layers, collaborate with product and infrastructure teams, support production systems, and mentor colleagues across telecom and AI engineering.

Conducts applied AI research for developer tools, building datasets, training large-scale machine learning models, and running experiments involving LLMs, RAG, evaluation, and software engineering. Collaborates with Microsoft Research, GitHub, product teams, engineers, and designers to advance AI capabilities and transition research into products.

Build and operate large-scale machine learning inference infrastructure for recommendation models, embedding-generation models, and self-hosted LLMs. Responsibilities include model optimization, export, serving, hosting, LLM fine-tuning, reinforcement learning infrastructure, and inference performance improvements. The role supports highly available ML systems across Snap’s advertising, content, growth, camera, and other product teams.

Design, develop, and deliver large-scale AI services, cloud platforms, and distributed systems for enterprise agent applications. Build observability, evaluation, optimization, automation, and deployment-safety capabilities across AI lifecycles. Improve tooling for fine-tuning and research workflows, optimize model-data-infrastructure interactions, and ensure reliability, security, scalability, and operational excellence. Collaborate with CoreAI and Azure partner teams through design, implementation, code reviews, and production delivery.

Designs, builds, tests, and deploys cloud-based AI applications and LLM-powered agentic systems. Works directly with customers to address pain points, documents experiments, communicates research results, and mentors early-career team members. The role combines applied research, product development, production deployment, and customer-driven innovation.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Leads responsible AI assessments and governance programs for high-impact AI products and use cases. Evaluates risks, architectures, mitigation effectiveness, and product releases; develops scalable policies and requirements; manages cross-functional programs; supports incident response; and coordinates engineering, research, sales, policy, and marketing stakeholders to advance safe and responsible AI deployment.

Leads the engineering team responsible for Nordstrom’s personalization engine, including recommendation models, training pipelines, real-time serving, and complete-look experiences. Owns technical direction, delivery, reliability, model and data quality, incident response, and team development. Partners with product, data science, platform engineering, and business stakeholders while remaining hands-on with architecture, code reviews, and AI-native development practices.

Develop, train, optimize, and evaluate statistical and machine learning models using real-world datasets. Prepare and assess data, design controlled experiments, communicate findings, and connect technical metrics to business outcomes. Collaborate with engineering, product, and domain teams to operationalize models, improve AI system performance, and support scalable production deployments.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Convert research innovations into reliable product capabilities, develop reusable infrastructure, resolve performance and scalability challenges, contribute to architecture, and coordinate complex technical projects. The role also emphasizes security, privacy, AI safety, Responsible AI, cross-functional collaboration, and mentoring engineers.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Develop reusable infrastructure and services, integrate research innovations into reliable products, drive architecture, and solve scalability, performance, reliability, security, privacy, and Responsible AI challenges. Provide hands-on technical leadership and mentorship across complex engineering initiatives.

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Conduct AI and robotics research under mentorship, including building AI systems, developing datasets and benchmarks, reading scientific literature, and running experiments with real-world robots. Interns may work on foundation models, robot skills, simulation, robot control, reinforcement learning, world models, and agentic robotics systems. The role is a 12-week, full-time internship based in Seattle and intended primarily for PhD students.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads the design, experimentation, productionization, and optimization of machine learning, deep learning, generative AI, LLM, and agentic systems. Develops Python-based proofs of concept and production solutions, evaluates and tunes models, integrates generative AI into ML platforms, improves system performance, and partners with product and engineering teams. Acts as an ML subject matter expert while mentoring practitioners and influencing product and technical decisions.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Build and lead development of an agentic AI bidding platform for real-time digital advertising auctions. Responsibilities include designing scalable distributed systems, data validation frameworks, experimentation and simulation tools, MLOps capabilities, and high-availability services. The role involves collaborating with data scientists and product teams, managing incidents and on-call readiness, exploring multi-agent AI techniques, setting technical direction, and mentoring engineers.

Conduct research on efficient and agentic AI systems alongside Microsoft researchers. Collaborate on machine learning and systems research, evaluate model quality, speed, cost, and performance, develop or assess AI applications and agents, analyze experiments, present findings, and contribute to research publications or open-source projects during a 12-week internship.

Conduct AI research during a 12-week Microsoft Research internship, collaborating with mentors and researchers on areas such as foundation models, reinforcement learning, agents, NLP, machine learning, human-AI interaction, and continual learning. Interns develop and evaluate research approaches, contribute to research software and publications, present findings, collaborate across teams, and participate in the research community.

Conduct research on agentic AI and data memory systems, including graph-based methods, agent memory, workflows, evaluation, and LLM-based agents. Collaborate with researchers, present findings, contribute to experiments, and potentially publish peer-reviewed work during a 12-week internship.