Senior Software Engineer, Machine Learning, Discover Interest Modeling

Add predictive modeling of the interests and creators a user will engage with next. Train with multi-objective goals (engagement, satisfaction).Redesign the interest vocabulary for much higher precision, and evaluate learned alternatives to today's interest vocab.Build deep, sequence-based token models that learn from long user activity histories across Google products.Build offline evaluation that predicts online results (profile-quality autoraters, retrieval and ranking metrics), and run well-sliced live experiments.Design a shared token description, feature set, and training objectives across token types.Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), Recommender Systems, deep learning, or personalization.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production, and experience building architecture.Experience with deep learning architectures and related algorithms (e.g., Transformers).Preferred qualifications:Master's degree or PhD in Computer Science or related technical field.5 years of experience with data structures and algorithms.1 year of experience in a technical leadership role.Experience developing accessible technologies.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.Discover is Google's personalized interest feed. With daily active users, it is one of the largest feed recommendation products in the world.In this role, the profile captures each person's long-term interests, favorite creators and in-the-moment intent. As these models drive recommendations a day, among the most important personalization signals in downstream ranking, and feed personalization across Google.Every Discover feed starts with us, from a constantly refreshing corpus of articles, videos, creator posts and AI-generated content, our retrieval and pre-ranking stack selects the applicants most relevant to each person.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. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), Recommender Systems, deep learning, or personalization.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production, and experience building architecture.Experience with deep learning architectures and related algorithms (e.g., Transformers).