Senior Software Engineering Manager, Mobile Maps Innovation

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

Senior Software Engineering Manager for Mobile Maps Innovation at Google. This role involves leading strategy and execution for new ways users search and interact with the physical world, focusing on leveraging location history and on-device components to provide intelligent, predictive context. The manager will oversee feature development, drive user-facing product initiatives (e.g., monetization), manage cross-functional projects, and grow/mentor a team. A key aspect is driving GenAI adoption and managing the deployment of models like Gemini Nano on hardware for validation.

What you'd actually do

  1. Lead the strategy and execution for new ways people search and interact with the physical world, ensuring Maps remains an indispensable daily tool.
  2. Oversee the development of features driven by deep understanding of user location and routines to provide "just-in-time" value.
  3. Ability to steer a traditionally platform-focused team toward building user-facing products in Maps (e.g. Monetization). Own initiatives focused on sustainable monetization and user growth, balancing commercial goals with a premium user experience.
  4. Push and navigate challenging cross-initiatives, breaking down silos to deliver unified Google experiences.
  5. Independently grow, mentor, and scale a team, fostering a culture of technical excellence and psychological safety.

Skills

Required

  • Java
  • C++
  • Python
  • technical leadership
  • people management
  • team leadership
  • GenAI adoption
  • working with remote/distributed teams

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • on-device mobile implementation
  • performance and latency constraints of real-time location features
  • Geo
  • Search
  • Mobile (Android/iOS)-based products
  • developing on-device, TEEs (Trusted Execution Environments), Sealed Computing, and Sealed Memory.
  • scaling products with significant commercial or growth-oriented components.
  • running models (e.g., Gemini Nano) and prompts on actual hardware for real-world validation.

What the JD emphasized

  • Experience driving GenAI adoption for innovation and productivity.
  • Deep understanding of running models (e.g., Gemini Nano) and prompts on actual hardware for real-world validation.

Other signals

  • driving GenAI adoption
  • running models (e.g., Gemini Nano) and prompts on actual hardware for real-world validation