Senior Applied AI Lead, Talent

Netflix Netflix · Big Tech · United States · Remote · Engineering Operations

Senior Applied AI Lead for Netflix's Talent team, focusing on identifying, prototyping, and shipping AI-powered capabilities across the full Talent lifecycle. The role bridges Talent domain expertise with AI fluency, building working prototypes and translating them into production partnerships with engineering.

What you'd actually do

  1. Partnering with Talent teams to identify the highest-value AI opportunities.
  2. Defining problem statements, scope solutions, and prioritizing ruthlessly — distinguishing genuinely high-leverage AI applications from novelty.
  3. Designing and building working AI prototypes — from scoping the problem to putting a functional demo in front of stakeholders for validation.
  4. Use LLM APIs, agent frameworks, AI-native platforms, and workflow automation tools to rapidly iterate without requiring dedicated engineering resources.
  5. Translating validated prototypes into tight product requirements and partnering with engineering to move from proof-of-concept to production.

Skills

Required

  • 6+ years in product management, applied AI, technical solutions, or a closely related field
  • Experience on both a Talent/HR function and a product or technical team
  • Genuine track record of building things with AI
  • Experience taking solutions from concept through to production deployment
  • Demonstrated ability to influence senior stakeholders without authority
  • Strong understanding of Talent processes and their data and workflow characteristics
  • Fluency with modern AI tools and paradigms
  • Comfort with data
  • Ability to communicate with clarity

Nice to have

  • Familiarity with HR technology platforms (Workday, Eightfold, etc.) and their integration and API capabilities is a meaningful advantage.

What the JD emphasized

  • genuine AI fluency
  • building working prototypes
  • move fast
  • high judgment
  • take a prototype all the way to a production partnership
  • genuine track record of building things with AI
  • LLM APIs, prompt engineering, agent frameworks (e.g., LangChain, CrewAI, custom agentic workflows), and AI-native tools to create working prototypes without heavy engineering support
  • taking solutions from concept through to production deployment
  • strong understanding of Talent processes and their data and workflow characteristics
  • Fluency with modern AI tools and paradigms: LLMs, retrieval-augmented generation (RAG), agentic systems, multimodal models, and AI workflow orchestration
  • Comfort with data — you can query a dataset, interpret analytics, and design measurement frameworks for AI solutions without needing a data scientist for every question.

Other signals

  • prototyping AI solutions
  • shipping AI-powered capabilities
  • AI fluency across Talent