Top AI Jobs in Seattle, WA

The Senior Staff AI Builder will lead multi-squad technical areas, enhance AI strategies, mentor builders, and address systemic issues in healthcare communications.

Lead product-focused data science and analytics work: define success metrics, design and analyze A/B tests, investigate product and customer performance, build anomaly detection frameworks, and translate insights into product and roadmap recommendations while partnering with 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.

The Senior Manager, Machine Learning Science will lead a team focused on developing ML models for travel recommendations and ranking, optimizing the booking experience for bundled products, and mentoring ML scientists.

Lead design, launch, and lifecycle management of Pluralsight solutions (AI, Cloud, Security). Define expert POV, build curricula, labs, assessments, and blended learning programs. Own roadmap, metrics, GTM partnership with Sales and Marketing, and continuous iteration using data and SME collaboration to drive enterprise customer outcomes.

Lead and grow an AI Platform Enablement team to build a governed internal AI development environment. Define platform strategy, partner with security and infrastructure, drive adoption via enablement programs, and establish metrics for usage, onboarding, and control effectiveness. Act as an executive-facing leader delivering secure, scalable platforms and change management.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead research and development of LLM and generative AI solutions, design scalable real-time ML models, define priorities and metrics for deep learning and RAG, promote Responsible AI best practices, and drive adoption of new methods across teams.

Lead product strategy and roadmap for Qualtrics' Core AI Platform, building foundational capabilities (ontologies, agents, model infra, retrieval, observability, safety). Partner cross-functionally to define architecture, prioritize investments, drive implementation, and measure platform adoption, reliability, and AI quality for enterprise use.

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.

Lead AI-first UX design for agentic and conversational experiences: own design system patterns shipped as code, prototype agents with LLM APIs, ensure accessibility (WCAG 2.1 AA), monitor pattern adoption, mentor peers, and deliver rapid end-to-end product designs that balance autonomy, safety, and human‑in‑the‑loop controls.

Lead design and delivery of AI/GenAI solutions and scalable data infrastructure. Drive business development, manage multiple client engagements, mentor teams, implement AI models using Python and C++, ensure data quality, and promote innovation across projects.

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

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Design, build, and operate autonomous infrastructure AI agents on Airwallex's Quartermaster platform to automate SRE, DevOps, and DBA workflows. Integrate agents with Kubernetes, Terraform, cloud APIs, monitoring, databases, and CI/CD. Define agent architecture, safety guardrails, evaluation metrics, and collaborate with SRE and platform teams to deploy reliable, auditable autonomous systems in production.

Lead Headway's AI platform vision and roadmap, own the internal assistant Eddy, write evals and prompts, configure agents and integrations, and partner with CX, Ops, Sales, Finance, and Clinical teams to identify high-value AI workflows and drive adoption across the company.

Lead product strategy and lifecycle for AI infrastructure platforms, prioritizing scalability, resiliency, compliance, and customer value. Drive discovery, market research, and backlog management; define KPIs (cost, reliability, risk posture); partner with engineering and cross-functional teams to deliver GPU-enabled model training, inference, and workload scheduling solutions for enterprise clients.

Lead strategy and execution of an AI-native product-led growth motion for ITSM: own the AI customer journey, build telemetry and experimentation, design onboarding and PQL models, partner cross-functionally to drive activation, adoption, expansion, and measurable revenue and lifetime value growth.

Lead end-to-end product for voice protection and conversational AI features serving consumers and telecom operator partners. Define product vision, prioritize roadmap, work closely with engineering and research on model training and prototyping, engage customers to validate needs, support commercial and legal partners, and drive product economics and successful launches.

Design, build, and deploy end-to-end ML solutions for ranking, recommendations, search, and personalization. Develop scalable data pipelines, monitoring, and evaluation frameworks; run A/B tests and collaborate with product, engineering, and analytics to drive data-driven decisions and reliable production models.

Lead architecture and development of production-grade agentic AI and multi-agent systems for reliability engineering. Design autonomous agents, manage AI infrastructure and deployment, ensure observability/security/guardrails, automate remediation, evaluate vendors and models, and produce secure, high-quality production code to improve system reliability and engineering productivity.

Lead design and delivery of reliable, scalable AI/ML platform services. Build production-grade code, reusable APIs/SDKs, observability and resilience tooling, define reliability standards, support on-call incident resolution, and mentor engineers to enable secure, compliant generative AI use cases.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

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

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.