Staff Software Engineer, AI/ML Recommendations, Rankings, Predictions, Google Ads

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.

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.

As a Staff Software Engineer in AI/ML Recommendations, Rankings, and Predictions for Google Ads, you will be responsible for designing, developing, testing, deploying, maintaining, and enhancing large scale software solutions. You will provide technical leadership on high-impact projects, manage project priorities, deadlines, and deliverables. You will also facilitate alignment and clarity across teams on goals, outcomes, and timelines, and influence and coach a distributed team of engineers. A key aspect of your role will be leading the design and implementation of recommendation systems, optimizing ML infrastructure, and guiding the development of model architecture.

This role requires a strong background in software development, data structures/algorithms, and extensive experience with recommendation systems models and ML infrastructure. You should be versatile, display leadership qualities, and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.