Senior Software Engineer, Ads Bidding ML

Develop and optimize bidding models to enhance and simplify through advanced machine learning models.Innovate and iterate on machine learning model design, improving quality, stability, and efficiency across the entire model lifecycle—from concept to deployment.Solve complex machine learning related problems by designing, running, and analyzing experiments using investigative and statistical methods.Write and test product or system development code.Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with software development in C++ or Python.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience with one or more of the following: sequence modeling, deep learning, natural language processing (NLP), reinforcement learning, or building machine learning models in production.Preferred qualifications:Master's degree or PhD in Computer Science, Mathematics, or a related science or technical field.5 years of experience with data structures and algorithms.1 year of experience in a technical leadership role.Experience with ads bidding, applied mathematics, operations research, or feedback loops.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. We build and maintain Machine Learning models using AI and ML techniques to predict user interactions on Search Ads. These models are a key component in setting advertisers' bids, with the goal of improving both satisfaction and Return on Investment (ROI) for Search Ads advertisers using Auto-bidding products. By optimizing towards advertisers' objectives, Auto-bidding products drive Google's global Ads business.In this role, you will be involved in the full machine learning model lifecycle, from design and training to deployment and serving models in production at the scale of Search Ads. This role offers the opportunity to innovate while collaborating with other teams, including research teams, to test and implement the latest technologies in our models.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: $174000 - $253000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.5 years of experience with software development in C++ or Python.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience with one or more of the following: sequence modeling, deep learning, natural language processing (NLP), reinforcement learning, or building machine learning models in production.