Staff Software Engineer, Youtube Ads Marketplace Optimization

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

Staff Software Engineer at Google YouTube Ads, focusing on optimizing ad marketplace through large-scale AI systems that model user interests. The role involves leading ML/AI/SW engineers, designing and developing recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Experience with production recommendation systems, ML design, ML infrastructure, and integrating generative AI tools is required.

What you'd actually do

  1. Design, develop, test, deploy, maintain, and enhance large scale software solutions.
  2. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  3. Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
  4. Lead the design and implementation of recommendation systems, optimize ML infrastructure, and guide the development of model architecture.

Skills

Required

  • software development
  • software design and architecture
  • building and deploying recommendation systems models
  • ML design
  • ML infrastructure
  • integrating generative AI tools or LLM interfaces

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures/algorithms
  • technical leadership role leading project teams and setting technical direction
  • working in a complex, matrixed organization involving cross-functional, or cross-business projects

What the JD emphasized

  • 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production
  • 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
  • Experience integrating generative AI tools or LLM interfaces into workflows

Other signals

  • large-scale AI systems
  • multi-task learning
  • recommendation systems
  • ML infrastructure
  • model architecture