Senior Software Engineer, Recommendations, Rankings, Predictions, Search Discover

Senior Software Engineer, Recommendations, Rankings, Predictions, Search Discover corporate_fare Google place Mountain View, CA, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

Minimum qualifications:

Bachelor's degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages. 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. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). 3 years of experience with data analysis, data manipulation, and statistical methods.

Preferred qualifications:

Master's degree or PhD in Computer Science or related technical field. 5 years of experience with data structures and algorithms. Knowledge of machine learning concepts, algorithms, best practices and building large language models. Excellent coding skills in Python and C++. Excellent problem-solving and investigative skills, with effective collaboration, communication and team-work skills. About the job 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're building the world's most intelligent content recommendation engine, using advanced Deep learning models like DeepNow, exploring LLMs, and developing novel ranking paradigms. In this role, you will manage some of the most challenging problems in AI-driven personalization. Your work will directly influence what millions of users see daily, optimizing for engagement, satisfaction. We thrive on innovation and impact. If you're eager to work on AI at scale, this is your team. 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. The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Develop and test new ranking algorithms and model features Analyze experiment results to measure impact and guide improvements. Collaborate with other engineers and researchers to implement new ideas. Contribute to the quality and reliability of the ranking systems. Explore new techniques to advance content understanding and personalization. Design and implement recommendation systems models across different domains, leverage ML infrastructure, and contribute to architecture design.