Minimum qualifications: Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 5 years of experience with ML/AI algorithms and tools, deep learning or other AI/ML modeling. Preferred qualifications: 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex organization involving cross-functional, or cross-business projects. Experience with advertising products, quality work in ads, or with statistical modeling and experimentation/analysis. Experience in quality or modeling engineering teams. 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. App Ads is one of the fastest growing businesses in Google Ads, with an annual run rate of over $20B. Our business drives a significant portion of business growth for Google overall. It affects the lives of users around the world by helping them find new games, plan vacations, and shop for great agreements 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. We focus on building machine learning models to predict users’ interaction with ads, and combining them with principled optimization techniques to help publishers monetize their content and advertisers acquire engaged users. Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Design new deep learning based ML models to predict users’ interaction with different ad formats across Google’s various ad delivery channels. Incorporate and engineer input features from ad creatives, publisher pages/content, as well as users’ personalized ad history to improve models’ accuracy. Improve the efficiency and reliability of ML models by designing novel model architectures and adopting development and release processes. Debug and troubleshoot advertiser performance problems. Work with Product Management, UX, and partner teams to design and deliver new ad experiences. Leverage data and metrics to help evaluate product success and drive business decisions.