Sr Product Owner, AI Productivity
$165,000–$215,000 year
On-site · Mountain View Santa Clara County, California, United States
Job Summary
Senior Product Owner to drive organization-wide AI adoption across Samsung Ads, owning the strategy, roadmap, and delivery of internal AI productivity capabilities. Serve as the bridge among AI/ML engineering, enterprise IT, and business stakeholders, translating AI capabilities into measurable gains in organizational productivity. Define phased AI adoption across engineering, product management, operations, sales, and leadership; establish governance, security, cost controls, and compliance; develop citizen developer programs; create measurement frameworks with adoption metrics and ROI tracking; own the product backlog for the internal AI platform (gateway, routing, prompt libraries, integrations, self-service infrastructure); partner with ML/infrastructure engineers on build-vs-buy decisions; run pilot-measure-scale cycles; manage multi-tool strategy (LLMs, coding assistants, document AI, workflow automation); design training programs for user personas; build communities of practice to promote adoption; remove friction through onboarding and self-service tooling; monitor usage to close gaps between access and productive use; define and enforce AI usage policies; mitigate risks like hallucination and data leakage; collaborate with Legal, Security, and Compliance on data handling and IP protections; ensure compliance in regulated environments and scalable DX.
Required Qualifications
- 8+ years in product management or product ownership
- at least 3 years focused on internal tools, developer platforms, or enterprise productivity
- experience driving adoption across large, distributed organizations (1,000+ people)
- track record of building self-service platforms that scale
- experience managing product backlogs and cross-functional delivery in Agile environments
- hands-on experience with AI/ML products
- strong understanding of LLM capabilities, prompt engineering, and AI tool evaluation
- familiarity with AI coding assistant landscape (Claude, Copilot, Codex) and enterprise deployment patterns
- ability to hold technical conversations with ML engineers and evaluate build-vs-buy tradeoffs
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