Staff Software Engineer, Ai/ml, Search Ads

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

Staff Software Engineer at Google working on AI/ML for Search Ads. The role involves leveraging AI/ML advancements, including LLMs and Agentic AI, to optimize search results and create new ad experiences. Responsibilities include designing, building, and maintaining self-service tools for AI/ML quality evaluation and architecting systems for deploying and serving AI/ML models for data quality assessment.

What you'd actually do

  1. Optimize the Search results page (including organic results and ads) to co-evolve ads, leverage AI to maintain business generation, create ad experiences, help users find content faster, and enable growth opportunities for advertisers.
  2. Leverage AI/ML advancements, including Large Language Models (LLMs) and Agentic AI, to solve optimization problems across search ads, from product innovation to agentic automation.
  3. Deliver ad experiences in the world of AI.
  4. Design, build, and maintain self-service tools and platform features that embed AI/ML quality evaluation methods.
  5. Architect systems for deploying and serving AI/ML models specifically for data quality assessment tasks.

Skills

Required

  • software development
  • Java
  • C/C++
  • Python
  • ML design
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • multimodal machine learning
  • LLMs
  • building and launching products
  • building, experimenting, and deploying AI-first systems and processes into production

Nice to have

  • applied AI/ML
  • model development
  • fine-tuning
  • application and evaluation
  • Generative AI/LLMs for user-facing problems
  • full-stack systems
  • self-service platforms
  • search/ads
  • quality systems
  • latest research and trends in AI/ML
  • data quality and assessment
  • GenAI data
  • evaluation methodologies
  • data quality best practices

What the JD emphasized

  • AI/ML quality evaluation methods
  • data quality assessment

Other signals

  • Leverage AI/ML advancements, including Large Language Models (LLMs) and Agentic AI, to solve optimization problems across search ads
  • Deliver ad experiences in the world of AI
  • Design, build, and maintain self-service tools and platform features that embed AI/ML quality evaluation methods
  • Architect systems for deploying and serving AI/ML models specifically for data quality assessment tasks