Program Manager Ii, Ai/ml, Google Ads

Google Google · Big Tech · Mountain View, CA +1

Program Manager for Google Ads focused on improving user experience and ecosystem trust by reducing bad ads and scammy content. This involves integrating ML techniques, including Gemini/Agent, and managing the deployment of distilled student models from LLMs.

What you'd actually do

  1. Drive cross-functional planning, tracking, and execution across multiple core projects
  2. Lead program delivery for integrating personalized intent signals (pGood, pRel, pContext) directly into Text and PLA auctions to mitigate bad ad experiences before serving.
  3. Manage the deployment of lightweight student models distilled from massive LLM auto-raters to scale real-time user-context encoders and low-quality filtering
  4. With partner teams, roll out significant launches and brand protection rules to achieve a reduction in scammy impressions and an overall reduction in low-quality ads
  5. Oversee joint technical roadmaps and alignment with AI Experiences, Automation, Retail, and Ads UI partners, maintaining clear leadership tracking and risk mitigation

Skills

Required

  • program or project management
  • integrating generative AI tools or LLM interfaces into workflows
  • Machine Learning projects (real-time model serving, teacher-student distillation frameworks, contextual user signals)

Nice to have

  • managing cross-functional or cross-team projects
  • Search, Ads, or large-scale AL/ML infrastructure projects
  • driving joint roadmaps and dependency alignment
  • managing high-stakes OKRs, proactively mitigate risk, and streamline tracking

What the JD emphasized

  • Experience integrating generative AI tools or LLM interfaces into workflows
  • Experience with Machine Learning projects (such as real-time model serving, teacher-student distillation frameworks, contextual user signals, etc.)

Other signals

  • driving cross-functional planning and execution
  • integrating personalized intent signals into auctions
  • managing deployment of distilled student models
  • rolling out brand protection rules
  • overseeing technical roadmaps with AI partners