Staff Software Engineer, Ads Modeling, Personalization, and Quality

Staff Software Engineer, Ads Modeling, Personalization, and Quality corporate_fare Google place Mountain View, CA, USA bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

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 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. 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. The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . 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.