Sr. Product Support Specialist

Netflix•New York, NY

2d•$260,000 - $370,000

About The Position

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. We recently launched a new ad-supported tier to offer our members more choice in how they consume their content. This tier allows us to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply immersed in our content. Our Team: The Ads team builds the advertising systems and integrations that power the delivery of ads using our world-class content delivery ecosystem. Our team is new and faced with the ambition of building highly performant advertising systems while monetizing our incredible slate of content. The Netflix Advertising Product Operations team accelerates product outcomes by creating frameworks, tools, and processes that enable Product and Tech teams to deliver value to our customers. Within this team, the Product Support function is evolving rapidly, transitioning from an internal model to an externally facing, AI-native support operation that serves Ad Agencies, Brands, DSPs, and Publisher partners integrating with the Netflix advertising platform. As we scale that AI-native model, we need someone who can own the knowledge foundation it runs on. The Role: We are seeking a Senior Product Support Specialist to own the knowledge architecture and quality infrastructure behind our Ads Support AI agent. This is a high-impact individual contributor role for someone who has deep Ad Tech support experience and wants to apply it to a different kind of problem: turning institutional product knowledge into structured, reliable content an AI agent can act on, and defining how we measure and improve that agent's performance over time. You will work closely with the AI agent Product and Engineering team to translate support and partner pain points into agent capability requirements, while remaining fully dedicated to the Ads Product Support team. You will not be building the agent's underlying models. You will be defining what the agent needs to know, how that knowledge is structured, how Ad Product Support performance is measured and fed back to PM & Eng to improve the Ads product over time, and what gets built into the agent versus routed to a human support expert. This role reports to Manager, Ads Product Support and is part of the Ads Product Operations team.

Requirements

8+ years of experience in the Ad Tech industry, with significant depth in technical support and/or solutions engineering, ideally with exposure to both buy-side and supply-side ecosystems.
Experience structuring or curating knowledge content for retrieval systems: taxonomy design, information architecture, and content operations for a knowledge base, help center, or similar system.
Working fluency in how retrieval-augmented generation and AI agents consume and act on content (embeddings, retrieval quality, context windows, prompt design), sufficient to write clear requirements for engineering partners. This is a skill we expect strong candidates to build quickly; deep ML engineering experience is not required.
Experience defining or applying quality benchmarks and evaluation methods for a support, content, or AI system, and iterating based on measured outcomes.
Proven ability to write clear, structured requirements that translate ambiguous problems into buildable agent or workflow logic.
Strong understanding of ad-serving, programmatic, and CTV protocols and standards (VAST, VMAP, OpenRTB) sufficient to judge whether knowledge content is accurate and complete.
Proven ability to operate as a senior individual contributor, driving outcomes independently and influencing cross-functional teams, especially PM and Eng, without direct authority.
Excellent written communication skills, with comfort authoring documentation intended for both human readers and machine consumption.
Experience with support and knowledge tooling such as Zendesk, Confluence, or Jira. Familiarity with vector databases or knowledge management platforms is a plus.
Familiarity with ad tech platforms such as Google Ad Manager, FreeWheel, Xandr, The Trade Desk, or DV360 is highly valued.

Nice To Haves

Familiarity with vector databases or knowledge management platforms is a plus.
Familiarity with ad tech platforms such as Google Ad Manager, FreeWheel, Xandr, The Trade Desk, or DV360 is highly valued.

Responsibilities

Build and continuously refine the categorization, taxonomy, and structure of Ads AI Knowledge and Ads Agentic Context, so that content is retrievable, current, and unambiguous for automated resolution.
Define and own quality benchmarks and an evaluation methodology for agent performance (accuracy, confidence level, resolution rate, retrieval quality), and re-test as the knowledge base evolves.
Write clear requirements and specs for agents and sub-agents that automate knowledge maintenance, for example a sub-agent that monitors systems for new or updated product releases and release notes, determines whether Help Center or troubleshooting content needs to be created or updated as a result, drafts the change, and routes it to a queue for human review.
Own the governance and review workflow for AI-drafted or AI-updated support content, ensuring nothing is published internally or externally without the appropriate human review and approval.
Partner closely with the Ads AI agent PM and Engineering lead as the voice of support knowledge and quality in their roadmap, without sitting on their team.
Maintain deep subject matter expertise in Netflix's advertising products and ad tech ecosystem (ad delivery, programmatic integrations, creative workflows, measurement, etc.), including solving complex issues the agent can not resolve, as the foundation for accurate agent knowledge.
Analyze escalation and ticket patterns to identify knowledge gaps and prioritize what gets built into the agent's knowledge base next.
Collaborate with Engineering and Product to flag product bugs and gaps surfaced through this work.
Contribute to defining support metrics, SLAs, and quality standards as they relate to agent-assisted resolution.