Sr Product Owner, AI Productivity

Samsung Electronics•Mountain View, CA

3d•$165,000 - $215,000•Remote

About The Position

Samsung Ads is seeking a Senior Product Owner to lead the "AI for All" initiative — scaling AI tools, workflows, and governance across our global organization. You will own the strategy, roadmap, and delivery of internal AI productivity capabilities, transforming how every team — engineering, product, operations, and business — works. You'll serve as the bridge among AI/ML engineering, enterprise IT, and business stakeholders, translating cutting-edge AI capabilities into measurable gains in organizational productivity. Enterprise AI adoption is fundamentally a product and change management challenge, not just a technology one. As Samsung Ads scales AI beyond early adopters, we need a dedicated Product Owner who can drive organization-wide AI adoption. We've proven the model works with developers (43%+ AI generated code, 767% adoption growth). Now we need to extend this to every function in the organization.

Requirements

8+ years in product management or product ownership, with at least 3 years focused on internal tools, developer platforms, or enterprise productivity.
Demonstrated experience driving adoption across large, distributed organizations (1,000+ people across multiple geographies and time zones).
Track record of building self-service platforms that scale without linear team growth.
Experience managing product backlogs and cross-functional delivery in Agile environments.
Hands-on experience with AI/ML products — you've shipped AI-powered tools or managed AI adoption programs at enterprise scale.
Strong understanding of LLM capabilities and limitations, prompt engineering, and AI tool evaluation.
Familiarity with the AI coding assistant landscape (Claude, Copilot, Cursor, Codex) and enterprise deployment patterns.
Ability to hold technical conversations with ML engineers and evaluate build-vs-buy tradeoffs for AI infrastructure.

Nice To Haves

Experience building AI governance frameworks — cost management, security controls, compliance in regulated environments.
Hands-on experience with LLM orchestration tooling (LiteLLM, API gateways, model routing, token management).
Background in developer experience (DX) or internal developer platform teams.
Experience building communities of practice or champion networks at scale.
Background in advertising technology, media, or high-scale data platforms.
Advanced degree in Computer Science, Engineering, or Business — or equivalent practical experience.
You've personally used AI tools extensively in your own workflow and can teach from lived experience.

Responsibilities

Define and execute the phased AI adoption roadmap across all organizational functions — engineering, product management, operations, sales, and leadership.
Establish the governance framework: tool selection criteria, security policies, cost controls, acceptable-use policies, and compliance requirements.
Develop the citizen developer program — enabling non-engineers to build AI-powered automations and workflows without central team bottlenecks.
Create and maintain the measurement framework: adoption metrics, productivity KPIs, ROI tracking, and time-to-value analysis per use case.
Own the product backlog for the internal AI platform (AI gateway, model routing, prompt libraries, tool integrations, self-service infrastructure).
Partner with ML/infrastructure engineers on build-vs-buy decisions for AI tools (LLMs, coding assistants, document AI, workflow automation).
Run pilot → measure → scale cycles for new AI capabilities across different teams and geographies.
Manage the multi-tool strategy (Claude, Copilot, Codex, Gemini) including vendor relationships, licensing, and integration architecture.
Design training and enablement programs tailored to each user persona (developer, PM, operations, leadership).
Build an internal community of practice — AI champions, office hours, showcases, shared prompt libraries, and best-practice documentation.
Remove adoption friction through onboarding flows, self-service tooling, documentation, and tiered support channels.
Track and close the gap between tool access and actual productive usage — drive active engagement, not just provisioning.
Implement cost controls and usage monitoring across AI services (model routing, token budgets, department chargeback models).
Partner with Legal, Security, and Compliance on data handling policies, IP protection, and regulatory requirements.
Define and enforce the organization's AI usage policies — what can and cannot be processed through AI tools.
Monitor for and mitigate risks: hallucination in critical workflows, over-reliance, shadow AI, data leakage.