- Content understanding algorithms: AI models that extract key information (e.g., editorial and caution tags, maturity ratings and advisories, and timecode-level annotations) across titles, episodes, and clips, along with the taxonomy principles that enable algorithm development.
- Content understanding workflows and platforms: a unified, scalable platform where experts review AI output, and where the teams that rely on that output can understand its quality, coverage, and impact.
Today, much of this work is still human-first. Your focus will be on moving us to AI/automation-first workflows, where models do the first pass and people focus on exceptions and quality, without compromising the accuracy and trust our members, partners, and regulators rely on. You will establish how we measure the quality and impact of our content understanding, and use it to decide what to automate, what to improve, and what to retire.
Looking ahead, you will shape what content understanding at Netflix can become. Beyond tags and ratings, you will identify new types of understanding that unlock new member experiences, for example in search, discovery, and personalization, as well as new use cases across the business.
You will partner with a cross-functional team of stunning colleagues across engineering, applied science, data science, and design, and work closely with a broad set of internal stakeholders: the content experts who review and annotate our content, the teams that consume content understanding signals, and our upstream partners on the content side.