Top AI Engineer Jobs in Seattle, WA

Lead the design and optimization of GPU-cluster infrastructure supporting AI and ML research. Partner with researchers, data engineers, and DevOps teams to identify bottlenecks, improve researcher efficiency, and build scalable, highly available systems. Drive infrastructure roadmaps, optimize resource utilization, define performance metrics, and oversee distributed training operations across HPC environments. The role requires deep expertise in accelerated computing, storage, scheduling, networking, containers, cloud platforms, and AI/ML frameworks.

Build and operate production AI-powered developer tools, including code review assistants, test generators, deployment diagnostics, and incident-response agents. Develop the self-service platform supporting deployment pipelines, observability, security, cost management, and reliable AI systems. Integrate LLM providers, agent frameworks, MCP servers, CI/CD, and infrastructure platforms. Own features from design through production, participate in on-call, improve operational practices, collaborate cross-functionally, and mentor engineers.

Set technical direction for AI-powered developer tools and a self-service AI platform. Design, build, and operate production AI agents, scalable distributed systems, deployment frameworks, observability integrations, security controls, and CI/CD integrations. Establish LLM and agent-development standards, lead cross-team initiatives, mentor engineers, conduct design reviews, measure AI-tool effectiveness, and participate in on-call operations.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

Build and deploy AI-enabled workflows across internal departments, partnering with business leaders and subject-matter experts. Design agentic solutions, evaluations, integrations, tools, and reusable platform capabilities. Establish operating models for discovery, onboarding, adoption, production support, and continuous improvement. Translate deployment insights into enterprise AI platform investments while advising senior technical and business stakeholders.

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Designs, builds, debugs, and optimizes scalable AI systems, including inference infrastructure, APIs, distributed services, and platform components. The role delivers secure, reliable, high-performance AI experiences across audio, image, video, and safety applications. Responsibilities include full-stack development, cross-team collaboration, architectural innovation, rapid issue resolution, and end-to-end feature delivery for systems serving millions of customers.

Build and operate DAT’s shared AI platform, including model gateways, inference services, feature and vector storage, evaluation systems, observability, and agentic frameworks. Architect secure, scalable AWS infrastructure; optimize model cost and latency; implement governance, monitoring, and quality controls; establish engineering standards; lead incident response; and mentor engineers and data scientists. The role requires technical leadership across teams and production experience with distributed systems, LLMs, machine learning platforms, and cloud infrastructure.

Lead architecture and technical direction for reliability, observability, and operational health across a globally distributed AI platform. Design telemetry, health monitoring, capacity management, routing safeguards, overload protection, progressive delivery, fault testing, automated rollback, tracing, diagnostics, and recovery capabilities. Drive cross-team initiatives, mentor engineers, improve production practices, and build scalable infrastructure for AI inference and distributed services.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Lead the architecture and technical vision for production-grade agentic AI applications. Design multi-agent orchestration, memory systems, reasoning pipelines, reliability infrastructure, observability, and high-availability distributed systems. Establish engineering standards, mentor senior engineers, conduct architecture reviews, support hiring, and collaborate with Product, Research, Platform, and AI Ops teams to scale LLM-powered workloads.

Design, implement, and ensure quality of systems stack, tools, and SDK integration for AI frameworks. Collaborate with hardware and ML teams, apply engineering principles, write low-level/high-performance code, define architecture, estimate scope, and drive coding standards.

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

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, build, and operate AI-powered automation, CI/CD systems, distributed services, developer tooling, and code-intelligence platforms. Own significant capabilities from architecture through production, improve engineering workflows using measurable evaluations, scale reliable automation and delivery systems, and operate secure multitenant services. Lead technical reviews, define reliability practices, integrate platforms through APIs and MCP, mentor engineers, and drive ambiguous technical problems to dependable production outcomes.

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.

Build and deploy data integrations, AI-powered workflows, and internal tools. Responsibilities include connecting APIs and databases, improving data quality, implementing validation and monitoring, developing retrieval and automation applications, translating business needs into deliverables, deploying production solutions, documenting systems, and transferring knowledge to internal teams.

Lead technical sales and consulting engagements that help enterprise customers adopt Microsoft AI and cloud technologies. Conduct proofs of concept, hackathons, coding sessions, and architecture workshops; design secure, scalable, cloud-native AI solutions; resolve technical blockers; guide production deployments; collaborate with engineering teams; and promote AI Foundry, Azure AI, agentic AI, Responsible AI, and modern application architectures through customer relationships and thought leadership.

Build and deploy production AI agents using tool-calling LLMs, structured outputs, API orchestration, and streaming responses. Develop evaluation harnesses, monitor quality and traces, and improve reliability, latency, and cost through caching, token budgets, retries, and observability. Create validated agent tools and integrations while collaborating with product, mobile, and backend teams. Own the full lifecycle from prototyping and evaluation through staging, production deployment, monitoring, and continuous improvement.

Prototype and develop generative AI applications for business software, including automated model evaluation, synthetic video, and consistent image and brand asset generation. Own projects from experimentation through product delivery, combining system architecture, quantitative reasoning, product judgment, and creative LLM application development.

Design, build, deploy, and optimize AI/ML and generative AI solutions on Databricks. Develop scalable Spark and Delta Lake data pipelines, operationalize models with MLflow, and build RAG and LLM applications. Implement MLOps practices including CI/CD, monitoring, governance, and model deployment. Collaborate with technical, data, engineering, and business teams to deliver enterprise-scale AI platforms and analytics solutions.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Build scalable, cost-efficient data pipelines and processing systems for AI model training. Responsibilities include discovering audio data sources, creating reliable ingestion pipelines, optimizing data quality and throughput, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, Python and Bash proficiency, and independent problem-solving.