Top Machine Learning Jobs in Seattle, WA

Build and scale customer-facing generative AI applications for Atlassian Rovo Chat. Develop agent harnesses that improve quality, reliability, and latency; apply advanced machine learning innovations; lead complex projects from technical design through launch; mentor senior engineers; and collaborate cross-functionally with internal teams and customers.

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.

Build and deploy enterprise-scale GenAI, RAG, multimodal, and multi-agent systems for growth marketing. Responsibilities include LLM training and adaptation, memory and retrieval architectures, behavioral embeddings, agentic interfaces, observability, safety, evaluation, and scalable production inference. The role requires end-to-end AI product development, collaboration with product and engineering teams, technical leadership, mentoring, and external representation through publications, patents, conferences, or open-source contributions.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

Build enterprise-scale machine learning and agentic search systems enabling AI agents to discover, retrieve, evaluate, and synthesize information. Work spans retrieval, ranking, LLM reranking, multi-step search, tool intelligence, grounding, citations, evaluation, and production infrastructure while addressing permissions, freshness, latency, trust, and multi-tenant requirements.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

The job description content is missing. Based on the title, this role involves senior-level machine learning engineering. Atlassian offers flexible work arrangements, location-based compensation, benefits, bonuses, commissions, and equity.

This is a senior machine learning engineering role at Atlassian, but the job description contains no specific responsibilities or qualification details. The position offers flexible work arrangements where Atlassian has a legal entity and includes location-based compensation, benefits, bonuses, commissions, and potential equity.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

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 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.

Leads the design, development, and scaling of machine learning systems for signals, attribution, recommendation, ranking, and advertising measurement. Provides technical direction, improves ML infrastructure, collaborates with cross-functional leadership, and promotes scalable, reliable, cost-effective engineering practices. The role requires shipping advanced models, mentoring engineers, solving ambiguous technical problems, and influencing Snap’s broader machine learning strategy.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design, train, fine-tune, optimize, and deploy large-scale machine learning systems for cloud security use cases. Build end-to-end ML pipelines, develop transformer and embedding models, productionize open-weight language models, and optimize inference for latency, cost, and quality. Architect resilient ML services across AWS and GCP using cloud-native microservices while collaborating with engineering teams on AI strategy and solving complex problems involving massive datasets.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads AI system evaluation and experimentation for Microsoft 365 Copilot. Develops metrics, scalable data pipelines, dashboards, and analytical tools; applies statistics, machine learning, and data mining to large datasets; investigates quality regressions; partners with engineering, research, and product teams; communicates findings; establishes responsible AI measurement practices; and mentors other scientists.

Develop and test AI/ML software solutions with cross-functional teams. Apply engineering principles, responsible AI practices, machine learning techniques, and software quality standards. Collaborate with stakeholders to define requirements, learn emerging technologies, improve system reliability and performance, and deliver projects in a cooperative environment.

Build and operate large-scale machine learning systems for ads personalization, including ad ranking, conversion-rate prediction, recommendation, behavioral modeling, and bidding optimization. Monitor model quality, calibrate predictions, address data drift and delayed feedback, and guide technical direction and project execution. The role requires collaboration with engineering leads and communication of complex technical concepts to non-engineering stakeholders.

Set scientific strategy for customer-grounded Copilot quality. Translate customer feedback, product signals, and incidents into evaluation and post-training priorities. Design representative evaluation sets, rubrics, graders, calibration methods, reward signals, and reusable learning assets. Diagnose failures across retrieval, grounding, tools, orchestration, reasoning, generation, and evaluation. Connect offline metrics with online customer outcomes, ensure rigor and reproducibility, and mentor scientists and engineers across organizational boundaries.

Develop, evaluate, and deploy AI-powered detection systems using language models, agents, and multimodal AI to identify phishing, fraud, scams, and other social engineering threats. Translate research into production solutions, design experiments, analyze model behavior and operational performance, define success metrics, and collaborate with research, engineering, and product teams to improve customer protection at global scale.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Leads the design, development, deployment, and measurement of large-scale machine learning, information retrieval, LLM, and agentic AI solutions. Defines scientific strategy, technical roadmaps, experimentation frameworks, and evaluation methods for web content quality, trust, safety, and understanding. Partners across engineering, product, and research teams, mentors scientists, influences organizational direction, and develops systems addressing spam, abuse, fraud, and low-quality AI-generated content.

Design and analyze experiments, causal inference studies, predictive models, and machine learning solutions. Evaluate AI and LLM-based systems, establish metrics and measurement frameworks, quantify uncertainty, and communicate insights through visualizations and data storytelling. Partner with engineering, product, and business teams to improve operational effectiveness and promote advanced analytics and experimentation practices.

Conduct data science research using machine learning, statistics, experiments, AI, and large-scale data to improve Microsoft products and services. Interns formulate analytical approaches, explore data, validate and monitor models, interpret results, collaborate with stakeholders, participate in peer review, and deliver actionable insights influencing customer and product outcomes.