Top Machine Learning Jobs in Seattle, WA

AI software engineering intern in Boeing’s Graduate Researcher Program, developing and evaluating learned models and contributing to machine learning applications for aerospace. The role involves preparing data, supporting machine learning development and deployment pipelines, and collaborating with distributed engineering teams. Candidates must be PhD students who have completed their first year by July 2027 and can work onsite full-time for 10–12 weeks during Summer 2027.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Lead research and development of perception algorithms for Physical AI, including detection, segmentation, tracking, localization, sensor fusion, and scene understanding. Define rigorous datasets, experiments, metrics, and evaluation pipelines, then optimize models for real-time deployment across Arm CPUs and AI accelerators. Collaborate with robotics, controls, hardware, product, and customer teams; mentor technical staff; guide on-device perception roadmaps; and contribute through publications, patents, and open-source work.

Lead the Ranking & Relevance team of software and machine-learning engineers. Own retrieval, ranking, recommendation, and personalization systems that match patients with therapists. Drive the transition from filter-based matching to outcome-aware machine learning, establish shared marketplace objectives, improve experimentation and model monitoring, and build evaluation and drift-detection capabilities. Hire and develop engineers, set technical standards, and partner with product, data science, payer, and provider engineering teams in a sensitive healthcare domain.

Build and optimize production-grade LLM and generative AI systems, including RAG pipelines, model fine-tuning, embedding search, evaluation, and scalable inference. Develop FastAPI services, integrate AI into production software, and improve latency, GPU utilization, reliability, and cost efficiency. Research emerging AI techniques, contribute to MLOps through model tracking and observability, and collaborate with clients and cross-functional teams.

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.

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.

Develop advanced algorithms and apply statistical, data mining, machine learning, and AI techniques to large structured and unstructured datasets. Identify trends, evaluate emerging data technologies, and communicate analytical findings to clients and ZS stakeholders. The role requires collaboration across multiple projects, strong quantitative and research skills, and proficiency with Python, SQL, Pandas, and PySpark. Client-facing work may require travel.

Conduct research in user modeling and personalization, including graph modeling, generative recommendation, personalization, and ML efficiency. Lead research projects, build scalable prototypes, evaluate large-scale machine learning systems, and publish findings at top conferences. The role also involves mentoring interns and junior researchers and translating advanced research into product improvements.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Develops optimization, simulation, predictive modeling, and machine learning solutions for outbound fulfillment, transportation, routing, capacity, and supply chain operations. Translates ambiguous business problems into mathematical models, validates solutions with large datasets, conducts experiments and scenario analyses, and partners with engineering, product, and operations teams to productionize models and improve network efficiency, cost, speed, and service levels.

Own the strategy, roadmap, architecture, and lifecycle for Qualtrics’ Core AI Understand layer. Lead capabilities spanning semantic systems, text analytics, prediction, simulation, benchmarking, agent infrastructure, retrieval, evaluation, observability, model infrastructure, governance, and safety. Partner with engineering, AI research, data science, design, product teams, and enterprise customers to define requirements, prioritize investments, measure quality and impact, and deliver reusable AI foundations across the company.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

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.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

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

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

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.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

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 design, development, and evaluation of multi-step agentic AI systems (autonomous agents, orchestration, tool use, memory, multi-agent collaboration). Integrate LLMs with external tools/APIs/databases, define evaluation frameworks, mitigate safety risks, and deliver production-grade solutions while mentoring ML staff.

Lead research, design, and production deployment of large-scale search and recommendation ML systems, including multi-modal retrieval, multi-stage ranking, personalization, sequential recommenders, evaluation (A/B testing, counterfactuals), and mentoring scientists while partnering with engineering and product teams.