Staff Software Engineer, Geminiapp Personalization, Deepmind

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

Staff Software Engineer at Google DeepMind on the Gemini App Personalization team, focused on building a next-generation AI assistant that deeply personalizes products for users. The role involves designing and building scalable features for capturing, organizing, and surfacing personal context, performing data analysis, developing evaluation techniques for contextual recall, owning model output quality, and contributing to a data flywheel. The position requires significant software development experience, particularly with big data, ML, LLMs, and data analysis, with a focus on creating a seamless cognitive extension for users.

What you'd actually do

  1. Design, prototype, and build scalable features on the full Gemini App stack that securely capture, organize, and intuitively surface long-term personal context.
  2. Perform data analysis of user feedback, logs, and evaluation tasks to identify opportunities for improving how effectively the assistant retains and synthesizes historical user interactions.
  3. Develop evaluation techniques (both automated and human-in-the-loop) to assess and hill-climb on the quality of contextual recall and the assistant's ability to act as a seamless cognitive extension.
  4. Act as the primary owner of model output quality, ensuring responses accurately reflect, synthesize, and build upon the user's unique history and ongoing preferences.
  5. Contribute to the development of a data flywheel that safely accumulates and leverages continuous user context, driving ongoing improvement and innovation.

Skills

Required

  • software development
  • testing
  • launching software products
  • big data analytics
  • machine learning
  • AI
  • large language models (LLM)
  • data analysis
  • data science
  • software design
  • software architecture

Nice to have

  • recommender systems
  • generative AI
  • information retrieval

What the JD emphasized

  • securely capture
  • seamless cognitive extension
  • primary owner of model output quality
  • safely accumulates

Other signals

  • building a personal AI assistant
  • continuously absorbs, organizes, and effortlessly recalls the unique interests, passions, and curiosities of individuals
  • seamless extension of their own intellect
  • model output quality, ensuring responses accurately reflect, synthesize, and build upon the user's unique history and ongoing preferences
  • data flywheel that safely accumulates and leverages continuous user context