Top AI Jobs in Seattle, WA

Leads company-wide AI adoption, operating the AI Center of Excellence, developing champions, running cross-functional AI sprints, delivering training, managing AI tools and budgets, and measuring workflow adoption, time savings, and financial impact. The role partners with Engineering to distribute shared AI skills, evaluates vendors, reports outcomes to executives, and publishes successful use cases across departments.

Lead product development and scaling for Microsoft Foundry model offerings: define growth metrics, run experiments, manage dependencies, build executive dashboards, and drive adoption, performance, and reliability across cross-functional teams.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills and availability, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Native or near-native Somali fluency, English proficiency, digital tool comfort, attention to detail, and curiosity are required. This is a talent community application, not an active job opening.

Join Welo Data’s Czech contributor network for flexible, remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and review cultural and linguistic accuracy. Native or near-native Czech fluency, English proficiency, residence in the Czech Republic or Slovakia, digital-tool comfort, curiosity, and strong attention to detail are required. This is a talent-network opportunity, not an active job opening.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.

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.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

The Director leads engineering and research for AI applications, managing interdisciplinary teams, driving technical roadmaps, and fostering collaboration across functions to deliver impactful AI initiatives.

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.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

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.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

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.

Lead Root’s AI Enablement Squad by defining AI-native ways of working, guiding cross-team adoption, connecting user workflows to AI Platform strategy, and turning experiments into reusable products and capabilities. Set technical direction, lead major initiatives, align senior stakeholders, establish adoption measures, promote responsible AI use, and mentor engineers and technical leaders across the organization.

Lead the AI Enablement Squad by prioritizing embedded AI adoption engagements, translating workflow discoveries into reusable AI Platform improvements, coordinating cross-functional partners, and coaching engineers. Establish repeatable adoption playbooks, protect capacity for scalable capabilities, communicate outcomes and blockers to stakeholders, and guide responsible deployment of AI solutions across the company.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead end-to-end delivery of document extraction and NLP solutions for payments: design scalable model pipelines, develop extraction/classification models, define evaluation and lifecycle practices, ensure compliance and explainability, and partner with cross-functional stakeholders to productionize and monitor ML systems.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead firmwide technology strategy for Data & AI, build partnerships with cloud and infrastructure vendors and the venture ecosystem, drive innovation adoption, evaluate and scale new technologies, raise internal awareness of Data & AI opportunities, support client-facing business development and strategic investments, and influence senior stakeholders to align partner roadmaps with JPMC objectives.

Develop, train, optimize, and deploy LLMs and multi-agent AI systems for tactical edge hardware. Interface agents with sensors, effectors, and drones, benchmark performance, and build reliable fail-safe production systems for defense customers.