Top AI Jobs in Seattle, WA

Owns end-to-end delivery of production agentic AI solutions for a business domain, including requirements discovery, LLM architecture, RAG pipelines, integrations, data engineering, experimentation, monitoring, and ROI validation. Leads relationships with business stakeholders, mentors forward-deployed engineers, and establishes responsible AI, privacy, security, and governance practices. The role also communicates outcomes to senior leadership and improves FDE delivery standards.

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 global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Develop AI agents, copilots, full-stack applications, backend services, and enterprise integrations for internal business workflows. Build React and TypeScript interfaces, automate data processing, integrate LLM APIs, write tests, troubleshoot defects, and support deployments. Collaborate with engineers and business stakeholders across Finance, Supply Chain, HR, Legal, and data center operations while following security, monitoring, documentation, and code-quality practices.

Own the product strategy, roadmap, and execution for DigitalOcean’s managed agentic AI infrastructure products. Define developer experiences, APIs, CLIs, documentation, pricing, packaging, and launch plans for services supporting long-running agents, tool execution, state management, secure sandboxes, identity, observability, and scale. Partner with customers, engineering, finance, security, legal, design, and go-to-market teams to turn emerging agent infrastructure needs into reliable, commercially successful products.

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.

Own an AI portfolio within a business domain, identify and prioritize opportunities, and build agentic solutions from prototype through production. Establish reusable patterns, evaluations, traces, and human-in-the-loop controls for increasing autonomy. Partner with business leaders, measure portfolio-level impact, and build production-grade applications across frontend and backend systems. Mentor engineers, review designs and code, influence platform choices, and communicate effectively with operators, engineers, and executives.

Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Lead and build Pfizer's AI-first design system across human and agent surfaces. Ship design standards-as-code, own conversational and agentic UX, drive adoption metrics, produce high-fidelity prototypes, advise executives, partner with AI/Product/Engineering, and grow a high-performing design team focused on safety, compliance, accessibility, and measurable experience outcomes.

Designs and delivers AI-powered experiences across Pfizer platforms, specializing in agentic AI and conversational UX. Responsibilities include experience strategy, prototyping with LLM APIs and agent frameworks, interaction and conversation design, human oversight and safety patterns, validation, accessibility, implementation partnership, reusable design systems, and outcome measurement. The role requires close collaboration with Product, Engineering, Data, and AI teams and ownership of complex initiatives from problem framing through production delivery.

Own the Responsible AI evaluation framework, evaluation program, and AI transparency standard. Define product vision, strategy, roadmap, features, user stories, and backlogs for AI evaluation tests and benchmarks. Partner with Legal, Privacy, AI engineering, and business stakeholders to prioritize evaluations, coordinate releases, monitor quality, and close findings. The role requires technical experience, product management expertise, Agile SDLC knowledge, and familiarity with Responsible AI frameworks and regulated AI applications.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads Mastercard’s global AI Engineering organization, overseeing enterprise AI product delivery from design through production and adoption. Builds reusable AI assets, agentic AI solutions, orchestration patterns, and workflow automation while ensuring reliability, scalability, security, governance, compliance, and operational excellence. Partners across technology, product, risk, and business teams to deliver measurable outcomes. Develops engineering leaders and high-performing global teams in a regulated enterprise environment.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. The role covers HPC, GPUs, rack-scale systems, private and hybrid cloud, Kubernetes, networking, storage, observability, security, reliability, governance, and automation. Responsibilities include solving complex scalability and performance challenges, defining engineering standards, evaluating emerging technologies, leading cross-functional initiatives, and mentoring engineers in a regulated environment.

Lead cross-functional analytics for MSN & Copilot Discover to drive engagement and revenue. Build forecasting, scenario-planning, and ML solutions, evaluate UX/monetization tradeoffs, inform product and monetization strategy, and communicate insights to senior stakeholders to influence investments and business outcomes.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Design, deploy, and optimize production-ready autonomous agent systems for customer applications. Build multi-agent workflows, tool-augmented LLM architectures, evaluation frameworks, safety guardrails, and persistent memory systems. Collaborate directly with startups, CTOs, and AI leaders to translate business needs into reliable AI solutions. Validate DigitalOcean’s AI infrastructure, identify platform improvements, and communicate deployment insights to product and engineering teams. Travel up to 30% for customer engagements and collaboration.

Design, build, and operate scalable services and distributed systems supporting agentic AI capabilities. Lead system, API, and data architecture; integrate AI/ML solutions across development and production environments; establish standards for reliability, security, testing, observability, and compliance; partner cross-functionally on technical direction; and mentor engineers while building reusable AI tools, orchestration layers, and safeguards.

Lead technical strategy with channel partners across ASEAN to build repeatable Microsoft Cloud and AI Workforce offerings (Copilot, Power Platform, Agents). Drive architecture design, workshops, POCs, offer development, partner enablement, and co-sell execution to convert technical capability into scalable revenue and customer outcomes.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.