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Google is seeking a software engineer to design and build scalable ML infrastructure for Ember’s 0-to-1 ranking and feed blending platform. You will apply advanced ML and optimization to curate a hyper-personalized feed for global users and lead GenAI-driven user experiences from ideation to production.
The role drives end-to-end ML life-cycle, including data gathering, model training, offline/online evaluation, and large-scale deployment. Collaboration with product and UX teams is essential.
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.
As a part of the Ember team, you will help reimagine Google Images, a platform serving over a billion daily interactions, to build a queryless, hyper-personalized feed that fuels creativity worldwide.
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.