Product Manager Ii, Enterprise Notebooks

Google Google · Big Tech · Sunnyvale, CA +2

Product Manager for Google Cloud's enterprise notebook platform (Vertex AI Workbench Instances), focusing on driving adoption, shaping the development environment, and integrating AI and agentic capabilities for data scientists.

What you'd actually do

  1. Drive the full lifecycle of Vertex AI Workbench Instances, from growth and adoption to the runtimes, images, and containers underneath, delivering the most secure, reliable, and performant data science development environment for enterprise.
  2. Own the strategy to win and retain enterprise data science teams on Workbench, turning a managed notebook platform into the default environment developers choose and organizations standardize on.
  3. Define how AI and agents show up in the enterprise notebook, from in-notebook assistance to agent-ready runtimes connected to the broader Google Cloud ecosystem, so data scientists move faster without leaving their trusted environment.
  4. Partner across the enterprise notebooks surface, including Colab Enterprise, to deliver a coherent experience so customers get one consistent, secure path across Google Cloud's notebook offerings.

Skills

Required

  • Product Management experience
  • Technical products from conception to launch
  • Building products for developers
  • Generative AI or Large Language Models experience

Nice to have

  • Master's degree in a technology or business related field
  • Business function or role experience
  • Technical presentations to executive leadership
  • Cross-functional collaboration with engineering, UX/UI, sales finance, and other stakeholders
  • Data science experience
  • Technical leadership

What the JD emphasized

  • enterprise notebook platform for data science and AI
  • AI and agentic capabilities
  • AI and agents show up in the enterprise notebook
  • agent-ready runtimes

Other signals

  • Product Manager for Vertex AI Workbench Instances
  • enterprise notebook platform for data science and AI
  • own the full lifecycle, from driving growth and adoption to shaping the runtimes, images, and containers
  • advancing how AI and agentic capabilities show up for data scientists
  • defining how AI and agents show up in the enterprise notebook, from in-notebook assistance to agent-ready runtimes