Top AI Jobs in Seattle, WA

The role involves architecting AI applications, delivering scalable solutions, conducting research, and implementing high-quality code for cloud platforms. Requires collaboration and effective communication.

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

Lead design and implementation of conversational and agentic AI solutions for clients, building voice and natural language interfaces, prototypes, and responsive web front-ends. Conduct user research and usability testing, collaborate with cross-functional teams to integrate AI platforms (Dialogflow, Amazon Lex, Azure OpenAI, AWS Bedrock), and apply human-centered design to optimize contact center workflows and customer experiences.

Lead development and implementation of conversational and agentic AI solutions for contact centers. Manage cross-functional teams, translate client requirements into technical specs, design responsive UIs using AngularJS/React/Vue, run usability testing, apply human-centered design, and mentor junior staff while building client relationships to ensure successful delivery of AI-driven products.

The role involves developing, testing, and validating Generative AI agents and maintaining automated testing standards. Responsibilities include mentoring junior associates, analyzing complex issues, and applying governance controls in AI-driven solutions.

Lead development and implementation of AI-powered contact center and conversational solutions. Guide cross-functional teams to design natural language interfaces using Dialogflow and Amazon Lex, prototype UIs with Figma, conduct usability testing and user research, validate data integrity and security, build stakeholder relationships, and coach teams to deliver high-quality, client-aligned AI products.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

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.

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

Own and build Gusto's AI assistant (Gus) and platform experiences end-to-end. Architect and implement scalable AI-native web experiences, create self-serve patterns for teams, collaborate with ML/product on prompts and escalation flows, run experiments, mentor engineers, and help scale a large Ruby on Rails and React application serving millions of users.

Lead a team of Machine Learning Engineers and Data Scientists to design and deliver ML systems at Remitly. Collaborate across domains to implement MLOps best practices and contribute to AI strategy.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead the Applied AI organization, architect AI-focused operating models, manage a team of AI engineers, and redesign business processes for improved efficiency and cost savings.

Use advanced analytics, statistical techniques, and machine learning to extract insights from large datasets. Design data solutions and pipelines, build predictive models (R, MATLAB), and apply TensorFlow and Scikit-Learn for deep learning and NLP. Collaborate with teams and clients, deliver quality work, and adhere to firm standards.

Lead the strategy and execution for DigitalOcean's agentic AI infrastructure: design and scale runtimes, sandboxed execution, tool and state management, launch revenue-driving SKUs, optimize efficiency and margins, and build a high-performing distributed engineering organization focused on reliability and operational excellence.

Lead development of AI-powered patient experiences: LLM-driven intake and therapist matching, between-session support, evaluation and safety infrastructure, outcome measurement, and HIPAA-compliant product delivery. Partner with Engineering, ML, Data Science, Clinical, and Legal to ship and monitor models in production, write evals, design red-teaming and human-in-the-loop flows, and measure patient outcomes.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

The Senior AI Engineer will design and build AI-powered automation tools, maintain LLM systems, and collaborate with business analysts to enhance processes across various operations.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

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.

Design and implement AI agents and Model Context Protocols to integrate enterprise systems (NetSuite, Zuora, Jira) with LLMs. Build secure, SOX-compliant automation and orchestration workflows, measure KPIs, and partner with Finance, IT, and Security to operationalize AI across enterprise applications.

Responsible for architecting and developing AI security infrastructure, optimizing performance and scalability of systems, and driving innovation in a collaborative environment.

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