Top Machine Learning Jobs in Seattle, WA

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

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.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Leads end-to-end architecture for scalable, secure AI solutions supporting Boeing’s supply chain. Designs hybrid cloud and on-premises data, AI, and analytics architectures; translates business needs into technical solutions; and guides modeling, ontology, orchestration, deployment, monitoring, governance, and visualization. Partners with business leaders, product managers, engineers, data scientists, and platform teams to develop machine learning, generative AI, agentic AI, deep learning, and decision-support capabilities.

Lead end-to-end predictive analytics initiatives, translating business objectives into data science solutions. Develop and deploy machine learning, deep learning, statistical modeling, and computer vision models for insurance use cases. Manage experimentation, model validation, monitoring, reproducible workflows, cloud deployment, production pipelines, and incident troubleshooting. Partner with stakeholders, communicate findings to senior audiences, document methodologies, and provide technical guidance through code reviews and best practices.

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 Boeing’s enterprise AI governance program by designing intake, review, risk-tiering, approval, and escalation workflows. Develop AI policies, standards, documentation, training, metrics, and executive reporting. Conduct AI risk assessments, evaluate vendor and generative AI systems, monitor regulatory requirements, and coordinate with engineering, legal, compliance, cybersecurity, and business stakeholders to ensure responsible, auditable AI adoption.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Build and maintain Cloudflare's new vector database offering: design, implement, and operate distributed, low-latency cloud systems. Own features across the stack, ensure reliability and performance, write design documents, and apply machine learning and data retrieval expertise using TypeScript, Go, Rust, and related technologies.

Lead and grow an ML engineering team responsible for the end-to-end ML lifecycle: data collection, preprocessing, model development, deployment, evaluation and monitoring. Research and implement scalable ML and generative AI techniques (recommendation and agentic systems), mentor and hire engineers, drive pragmatic, business-focused solutions, and communicate technical results to stakeholders.

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

Lead and grow a high-performing ML team to design and deliver end-to-end AI features for DevAI products. Drive LLM integration, prompt engineering, technical strategy, project delivery, stakeholder alignment, and process optimization while mentoring engineers and managing performance.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Leads a data science team and personally drives analyses, experimentation, and product analytics to identify patient experience improvements. Partners with Product, Engineering, Research, and Data Engineering teams to develop product features, evaluate business and product performance, guide product planning, and translate statistical findings into actionable recommendations. Manages and develops analytical talent while applying data storytelling, A/B testing, marketplace metrics, and retention, conversion, engagement, and churn analysis.

Lead design and adoption of cloud-based, portable AI platform architectures across hybrid environments. Advise on technology selection, document target-state designs, shepherd architecture reviews, develop multi-year roadmaps, integrate enterprise AI capabilities, produce secure production code, and align engineering with architectural governance and controls.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.