Top AI Jobs in Seattle, WA

Own the MLOps foundation for a shared enterprise AI platform. Build production code, infrastructure automation, delivery pipelines, model and prompt lifecycle tooling, serving systems, observability, evaluation, and cost controls. Establish technical guardrails, governance, security, and operational standards while embedding with teams to move AI workloads into reliable production. Provide principal-level technical leadership, mentor engineers, influence architecture, and communicate trade-offs with technical and executive stakeholders.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

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.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Own the strategy, roadmap, architecture, and lifecycle for Qualtrics’ Core AI Understand layer. Lead capabilities spanning semantic systems, text analytics, prediction, simulation, benchmarking, agent infrastructure, retrieval, evaluation, observability, model infrastructure, governance, and safety. Partner with engineering, AI research, data science, design, product teams, and enterprise customers to define requirements, prioritize investments, measure quality and impact, and deliver reusable AI foundations across the company.

Own end-to-end product direction for finance administration AI agents, including strategy, roadmap, agent behavior, prompting, evaluation, quality, and feedback loops. Partner with engineering, design, and research while working directly with finance administrators to build trustworthy systems for expense compliance, reporting, and financial analysis. Prototype, analyze traces and transcripts, measure performance, and determine when agents should recommend actions or act autonomously.

Lead AI/ML discovery product strategy and roadmap for search and recommendations. Partner with engineering, data science, design, and research to build AI-native discovery experiences. Drive experimentation and A/B testing, define metrics, write user stories and acceptance criteria, manage backlog and releases, own launch plans and post-launch validation, and influence stakeholders across the organization.

Leads enterprise AI enablement and intelligent automation, overseeing use-case intake, prioritization, delivery, governance, adoption, and value realization. Establishes the AI operating model, builds literacy and training programs, manages automation and AIM teams, sets responsible-AI guardrails, tracks adoption metrics, and aligns investment with enterprise transformation priorities. Requires executive stakeholder leadership, portfolio governance, team development, and experience scaling AI or automation capabilities in complex organizations.

Develops, configures, tests, and supports AI solutions, workflows, integrations, and model-enabled applications. Creates technical instructions and documentation, troubleshoots and optimizes AI systems, coordinates technical tasks, and collaborates with architects, delivery leaders, project teams, and stakeholders. Requires a college degree or equivalent, three years of related experience, technical communication skills, and the ability to travel and participate in on-call availability.

Partner with executives to understand business operations, identify and prioritize high-impact AI opportunities, define ROI, and create actionable roadmaps. Collaborate with engineers to scope solutions, inform architecture, maintain alignment with business outcomes, present recommendations to C-suite stakeholders, and manage handoffs to implementation.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Architect data-driven threat-detection strategies, translate complex security threats into mathematical problems, build probabilistic models and graph-analytics detections, and operationalize production-grade machine learning to minimize alert fatigue across the security organization.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.

Supports AI governance frameworks, policies, risk assessments, compliance reviews, monitoring, reporting, audits, and responsible AI practices. Partners with legal, compliance, security, AI, and business stakeholders to interpret regulations, establish controls, mitigate risks, and improve governance maturity. Tracks regulatory developments, maintains documentation, advises project teams, and promotes responsible AI adoption across the organization.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Lead go‑to‑market and close enterprise deals for AI/GenAI healthcare solutions across payers, providers, PBMs, and life sciences. Own revenue targets, shape solutions with architects and product teams, run POCs and demos, craft proposals and pricing, and present ROI/value to CXO stakeholders.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Evaluate and compare AI-generated responses across English and French content, assessing factuality, relevance, reasoning, clarity, safety, tone, cultural appropriateness, and instruction-following. Apply detailed annotation guidelines consistently, document evidence-based decisions, meet daily productivity expectations, complete qualification training, and incorporate feedback through calibration and quality reviews.

Evaluates AI-generated responses through side-by-side comparisons across text, search, file, image, and conversational tasks. Assesses factuality, relevance, reasoning, clarity, instruction-following, safety, tone, cultural appropriateness, and overall usefulness in English and French. Applies detailed annotation guidelines, documents evidence-based decisions, maintains accuracy and productivity across repetitive high-volume work, participates in training and calibration, and incorporates quality feedback.

Evaluate and compare AI-generated responses across English and Italian content, assessing factual accuracy, relevance, reasoning, clarity, instruction-following, tone, safety, cultural appropriateness, and overall usefulness. Apply detailed annotation guidelines consistently, document evidence-based decisions, meet productivity expectations, participate in training and calibration, and maintain quality across repetitive, high-volume assignments.

Evaluate and compare AI-generated responses across English and French content, assessing accuracy, relevance, reasoning, clarity, instruction-following, tone, safety, cultural appropriateness, and overall usefulness. Apply detailed annotation guidelines consistently, document evidence-based decisions, meet daily productivity targets, participate in training and calibration, and incorporate quality feedback. The role requires strong French expertise focused on France, English fluency, analytical judgment, attention to detail, and comfort with repetitive, high-volume evaluation work.

Own strategic customer relationships and lead deployment of AI workflows into supplier operations. Map customer SOPs and data sources, identify high-ROI AI opportunities, translate needs into product and implementation requirements, drive adoption, partner with product/engineering, build playbooks, track value, and surface expansion opportunities across global teams.

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