Product Manager, Workspace Search

Google Google · Big Tech · Bengaluru, Karnataka, India

Product Manager for Google Workspace Search, focusing on building AI-powered search offerings for human and agentic users, improving search quality, reducing latency, and implementing automated evaluation frameworks. The role involves leading the product lifecycle, defining strategy, and collaborating with cross-functional teams to deliver innovative search experiences across Workspace applications.

What you'd actually do

  1. Lead the product lifecycle for search within Workspace apps like Gmail and Chat, and other Workspace apps. Identify opportunities and deliver features to improve search quality, reduce latency, and enhance the user experience.
  2. Institute and oversee processes for rapid experimentation, evaluation, and iteration on search quality, leveraging user feedback, data analysis, and A/B testing.
  3. Drive the strategy and implementation of automated evaluation frameworks, particularly for novel search scenarios and greenfield projects, to ensure consistent quality and velocity.
  4. Define, track, and analyze key search success metrics to inform product decisions, measure impact, and communicate progress.
  5. Collaborate and influence a wide range of cross-functional teams, including Engineering, User Experience (UX), Research, Marketing, and Leadership, to build consensus and drive execution.

Skills

Required

  • 5 years of experience in product management or a related technical role
  • 2 years of experience taking technical products from conception to launch

Nice to have

  • Master's degree in a technology or a business related field
  • 3 years of experience in a business function or role (e.g., strategic marketing, business operations, consulting)
  • Experience in working with search and ranking technologies
  • Experience designing and successfully implementing automated evals, especially with new products with limited users and datasets
  • Experience scaling up AI technologies, domain knowledge in Artificial Intelligence/Machine Learning (AI/ML) and experience building and scaling Application Programming Interfaces (APIs)
  • Understanding of search and engagement metrics, and how to apply them to guide product development

What the JD emphasized

  • scale up Search for agentic users
  • build products powering AI-motivated experiences
  • automated evaluation frameworks
  • novel search scenarios
  • greenfield projects
  • automated evals
  • new products with limited users and datasets
  • scaling up AI technologies

Other signals

  • building AI-powered search experiences
  • scaling search for agentic users
  • driving strategy and implementation of automated evaluation frameworks