The role
From Google's own posting.
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.
App ads is one of the fastest growing businesses in Google Ads. Our business affects the lives of users around the world by helping them find new games, plan vacations, and shop for great deals or fun experiences, etc.
Our team delivers engineering innovations to improve the App Ads Quality and provide key technologies to empower App Ads business growth. The team is constantly exploring, developing and applying state of the art technologies to help publishers monetize their content and advertisers acquire engaged users. Our top project areas include ML/AI Agent driven App ads modeling and infrastructure optimization.
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 .
Write product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
Design and optimize deep learning based ML models to predict users’ interaction with different ad formats across Google’s various ad delivery channels.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
2 years of experience programming in Python or C++.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
1 year of experience with end-to-end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
Master's degree or PhD in Computer Science or related technical field.
2 years of experience with data structures and algorithms.
Experience in conducting quantitative data analysis, statistical modeling, interpreting complex datasets, or mathematical optimization.
Experience developing accessible technologies.
Bachelor’s degree or equivalent practical experience.
2 years of experience programming in Python or C++.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
1 year of experience with end-to-end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).