Staff Software Engineer, Ads Enterprise Platform

Google Google · Big Tech · Kirkland, WA +1

Staff Software Engineer on the Google Ads Enterprise Platform team, focusing on building a next-generation cross-channel ads buying platform powered by generative AI. Responsibilities include developing a multi-agent platform, managing model retraining, persona forecasting, and integrating AI into workflows. Requires strong software development, ML infrastructure, and generative AI experience.

What you'd actually do

  1. Drive the long-term engineering strategy, incorporating organizational business priorities and technological shifts while leveraging AI capabilities.
  2. Own key technical decisions based on your expertise to guide the team through important design tradeoffs.
  3. Improve the organization's technical health by ensuring the right investments are made to remove widely-felt barriers to productivity.
  4. Work cross-functionally to ensure alignment and re-use across the Ads platforms while making decisions that are best for the overall business.
  5. Grow the next generation of technical leaders through direct coaching and scalable mentorship initiatives.

Skills

Required

  • software development
  • software design and architecture
  • ML design
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine-tuning
  • prompt engineering
  • multi-agent systems
  • generative AI tools integration
  • LLM interfaces integration

Nice to have

  • technical leadership
  • complex, matrixed organization experience
  • cross-functional projects
  • cross-business projects
  • data structures
  • algorithms

What the JD emphasized

  • 5 years of experience with ML design and ML infrastructure (e.g., model deployment, evaluation, data processing, debugging, and fine-tuning).
  • Experience in prompt engineering, or multi-agent systems.
  • Experience integrating generative AI tools or LLM interfaces into workflows.

Other signals

  • building and tuning the multi-agent platform
  • instrumenting model retraining based on user actions
  • building digital consumer persona forecasting
  • long-term memory and session management
  • multi-user collaboration
  • integrating generative AI technology
  • prompt engineering
  • multi-agent systems
  • integrating generative AI tools or LLM interfaces into workflows