Staff Software Engineer, Machine Learning, Labs

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.

Labs is a group focused on incubating early-stage efforts in support of Google's mission to organize the world's information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.

As a Staff Software Engineer in Machine Learning for Labs, 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.

With your technical expertise, you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. You will provide technical leadership on high-impact projects, influence and coach a distributed team of engineers, and facilitate alignment and clarity across teams on goals, outcomes, and timelines.

This role offers a competitive salary range of $189,000-$284,000 + bonus + equity + benefits, with individual pay determined by work location and additional factors, including job-related skills, experience, and relevant education or training.