Top AI Product Manager Jobs in Seattle, WA

Owns the enterprise AI portfolio, product lifecycle, integrated roadmap, operating cadence, and cross-team delivery dependencies. Leads planning, portfolio reviews, executive forums, backlog management, prioritization, Agile ceremonies, risk resolution, and high-visibility initiatives. Partners with engineering, architecture, security, identity, and leadership teams to deliver AI products aligned with business goals, technical performance targets, employee satisfaction, and committed timelines.

Own the end-to-end enterprise AI training and adoption product lifecycle, including strategy, roadmap, training content, vendor coordination, product releases, and the AI Champion Network. Partner with technical teams, business leaders, vendors, and subject matter experts to drive safe AI adoption. Analyze usage, completion, and employee feedback data to prioritize improvements and measure outcomes across a distributed workforce.

Lead the vision, strategy, roadmap, portfolio, and go-to-market execution for AI inference cloud services. Partner with engineering, customers, sales, marketing, and industry partners to solve scaling and optimization challenges across specialized GPU infrastructure. Evaluate AI and cloud technology trends, define differentiated inference capabilities, translate customer needs into product investments, and drive business growth through innovative AI cloud products.

Own product strategy, roadmap, and business results for organic discovery and AI visibility across thousands of real estate websites. Lead engineering and design partners, translate customer needs into platform capabilities, measure search and AI citation impact, and connect outcomes to retention, revenue, and client growth. Partner cross-functionally with sales, marketing, and client success to launch and communicate product value.

Own the enterprise AI assistant product lifecycle, including vision, strategy, roadmap, capability onboarding, entry points, backlog, and production performance. Evaluate AI advancements, conduct research, define requirements, coordinate Agile delivery, manage quality and usage-based costs, and partner with security, privacy, governance, platform, business, and operations teams. Success is measured through routing accuracy, answer quality, cost per interaction, product performance, and employee satisfaction.

Own the product direction for Physical AI applications and the reusable ML and software stack running on Arm-based silicon. Define target workloads, requirements, interfaces, roadmaps, benchmarks, acceptance criteria, and release decisions across robotics, autonomy, AI inference, middleware, tooling, simulation, and deployment. Validate customer and partner needs, guide build-versus-partner choices, evaluate measured prototypes, and align engineering, architecture, product, and business leaders through delivery and launch.

Owns T-Mobile’s enterprise AI enablement product, including the roadmap, backlog, curriculum, learning paths, rollout communications, and champion network. The role improves employee adoption of GenAI tools and agents by creating technical and non-technical learning experiences, measuring usage and effectiveness, gathering feedback, and continuously refining content. It partners with HR Learning and Development while managing AI-specific enablement strategy and delivery.

Own Deepgram’s AI applications product line, including strategy, roadmap, customer definition, acquisition, activation, retention, monetization, and profitability. Lead zero-to-one product development, represent the product with customers and partners, and coordinate research, engineering, design, and marketing. Prototype and validate ideas through real usage and measurement while communicating decisions to executives. The role requires shipped, paid consumer or prosumer products, deep AI application experience, technical depth, design judgment, and strong product leadership.

Own internal AI and automation products that make Engine’s business functions faster and more scalable. Map workflows, identify high-value repetitive workloads, establish baseline metrics, partner with engineering to build and launch solutions, and drive adoption. The role requires evaluating LLMs and agents, incorporating human review, controlling scope and costs, measuring cycle-time improvements, and transitioning successful workflows to functional owners.

Leads product strategy and adoption for GitLab’s AI-powered software creation experience. Owns the journey from discovery and onboarding through activation, retention, conversion, and expansion across individual developers, teams, administrators, and buyers. Uses customer research, product analytics, funnel and cohort analysis, and experimentation to identify friction, measure outcomes, and improve growth. Partners cross-functionally across GitLab to create a coherent AI Software Factory experience and drive enterprise B2B product-led growth.

Own the strategy, roadmap, and execution of Docker’s AI Gateway, enabling secure model access for developer and enterprise AI agent workflows. Responsibilities include simplifying onboarding, shaping APIs and integrations, defining governance and access controls, managing model interoperability, optimizing routing and inference costs, and driving launches. The role partners with engineering, design, customers, security stakeholders, and inference providers.

Own the roadmap for cybersecurity exposure-reduction scenarios in Microsoft Intune. Define product requirements, partner with engineering, design, and security teams, and build AI-powered experiences that recommend or automate endpoint remediation. Engage enterprise customers, incorporate feedback, and measure adoption and exposure-reduction outcomes while balancing security improvements with operational impact.

Own the enterprise AI product roadmap, prioritizing use cases by value, ROI, and organizational impact. Lead AI platform evaluations, renewals, vendor negotiations, governance, and responsible-use standards. Partner with teams across the company to identify use cases, drive adoption and enablement, quantify outcomes, and communicate strategy and results to executives and employees.

Owns the strategy, roadmap, execution, launch, adoption, and continuous improvement of an AI security product domain within Cisco AI Defense. Partners with customers, engineering, design, security research, marketing, sales, and leadership to translate enterprise security and AI needs into scalable SaaS capabilities. Defines outcomes, prioritizes investments, guides technical trade-offs, and drives customer value across the product lifecycle.

Own the strategy, roadmap, adoption, and value realization of an enterprise Data and AI Platform centered on Azure Databricks. Translate engineering and AI team needs into reusable, governed, self-service platform capabilities. Partner with Engineering, Architecture, Governance, Security, and Product teams on lakehouse architecture, data quality, lineage, access, developer experience, and AI workloads. Drive platform adoption, onboarding, consumer satisfaction, reliability, and measurable business value.

Own the vision, roadmap, and delivery of customer-facing AI, predictive analytics, and SaaS data products. Partner with strategic clients, UX, data science, engineering, sales, and marketing to translate business needs into technical requirements, validate product hypotheses, and launch features that drive adoption, retention, and revenue expansion. Use SQL and data visualization tools to analyze data, communicate insights, and shape intuitive product experiences.

Define the vision, strategy, and roadmap for Microsoft’s Responsible AI platform. Lead development of capabilities for AI evaluation, red teaming, safety guardrails, risk detection, monitoring, governance, transparency, and compliance. Conduct enterprise customer discovery, translate emerging risks and regulations into scalable products, and coordinate execution across engineering, research, legal, security, compliance, and Microsoft product organizations. Establish success metrics, improve adoption, and represent Microsoft with customers, partners, standards bodies, and industry forums.

Own F5’s enterprise customer experience AI strategy, roadmap, governance, and delivery. Re-architect customer journeys using generative and agentic AI, establish standards for evaluation, security, privacy, oversight, and responsible deployment, and make build-versus-buy decisions. Prototype solutions, define technical requirements, drive adoption, measure operational and customer outcomes, and present recommendations to executives. The role spans go-to-market, service, self-service, renewal, partner, and internal CX workflows.

The Product Manager will lead AI solution strategy and execution across the Clover ecosystem. Responsibilities include prioritizing AI workflow use cases, translating business processes into agentic experiences, defining metrics and roadmaps, building evaluation and confidence-scoring frameworks, implementing governance and audit workflows, prototyping prompts and agent systems, and partnering with vertical product managers. The role requires extensive product management experience, strong generative AI expertise, analytical execution skills, and hands-on technical prototyping capabilities.

Leads the discovery, development, launch, and scaling of AI-first product capabilities. Translates ambiguous customer problems into hypotheses, requirements, evaluation criteria, roadmaps, and measurable outcomes. Partners with engineering, UX, tax SMEs, go-to-market, and customer success to prototype and operationalize reliable, compliant AI solutions. Owns AI quality, monitoring, performance, feedback loops, responsible AI practices, and continuous improvement while influencing cross-functional stakeholders and mentoring junior product owners.

Own the AI Services product strategy, roadmap, and execution for agentic workflows, MCP tools, connectors, and AI quality infrastructure. Define agent behavior, evaluation frameworks, integrations, metrics, and risk controls while partnering closely with engineering, architecture, security, legal, and consuming product teams. Drive adoption across the portfolio, make technical trade-offs, communicate outcomes to leadership, and deliver reliable AI capabilities for enterprise finance and accounting users.

Lead the end-to-end development of agentic AI products for workforce management, from operational research and agent definition through prototyping, evaluation, enterprise deployment, and commercialization. Define human-in-the-loop controls, tools, escalation rules, specifications, and release criteria. Build prototypes, prompts, evaluation sets, and code contributions while diagnosing model and system failures. Partner with enterprise design customers, engineering, AI Lab, executives, and go-to-market teams to deliver reliable, compliant products.

The Data and AI Product Manager owns product vision, roadmap, backlog, requirements, prioritization, delivery cadence, success metrics, adoption, governance, and stakeholder communication. The role partners with engineering teams and business units to deliver governed data and AI products, measure business value, guide product direction, and support engineering pod leadership. It requires substantial product management experience, SQL proficiency, data platform knowledge, and the ability to translate user and business needs into actionable product decisions.

Own the Field First Mobile App and future AI products from discovery through scaling. Responsibilities include field research, roadmap ownership, content governance, AI instruction and guardrail design, question-bank testing, vendor evaluation, requirements, pilot management, adoption, measurement, and executive reporting. The role partners with IT, HSE, Operations, HR, Fleet, vendors, and frontline leaders to deliver trustworthy AI assistance that improves safety support, onboarding, and operational efficiency.

Owns Storable’s AI agents and automations for voice and text interactions with storage prospects and tenants. Defines workflows, permissions, verification, approvals, escalation, configuration, evaluation criteria, quality thresholds, and launch gates. Partners with Operations, Support, Legal, Compliance, and the APIs and Developer Platform team. Requires experience shipping LLM-powered products that take real actions, understanding agent architecture and tradeoffs, and building prototypes.