Wholepage Optimization Software Engineer, Google Ads

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience programming in Python or C++.
  
• 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 2 years of experience with data structures and algorithms.
  
• Experience developing accessible technologies.
  
• Experience in Seach/Ads or general quality systems.
• Familiarity with the latest research and trends in AI/ML, particularly as applicable to data quality and assessment.
• Expertise in full-stack engineering and app development.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

As Search experience rapidly evolves with AI Overviews and AI Mode, the commercial engine that powers it must co-evolve. Our team sits at the intersection of user intent and monetization on search. We leverage cutting-edge machine learning and LLMs to reimagine the commercial journey across Google Search and Lens, ensuring we deliver value to users, advertisers, and Google’s long-term revenue. As part of the team, you will lead this effort and work on foundational, open-ended problems to build 10x better ads experiences, in partnership with teams across Ads, Search and GDM.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Leverage state-of-the-art AI/ML advancements, including LLMs & Agentic AI, to solve critical optimization problems across search ads, from product innovation to agentic automation.
• Drive 10x better ads experiences compared to what we have today in the world of AI.
• Identify gaps in existing engineering processes and solve using AI-driven automation.
• Design, build, and maintain self-service tools and platform features that embed these advanced AI/ML quality evaluation methods.
• Architect scalable systems for deploying and serving AI/ML models specifically for data quality assessment tasks.