Senior ML Software Engineer, Search, Discover, Ads

Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with software development in Python programming language, and with data structures/algorithms.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 Machine Learning (ML) infrastructure (e.g., model deployment, model evaluation, data processing, debugging etc).Preferred qualifications:Master's degree or PhD in Computer Science, or in a related technical field.2 years of experience with Machine Learning algorithms and tools (e.g., Jax, TensorFlow, deep learning, natural language processing, etc).1 year of experience in a technical leadership role.Experiences with recommender systems, Large Language Model (LLM), personalization, Natural Language Processing (NLP), and retrieval. 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.In this role, you will build the key personalized modeling, ranking, re-ranking and Large Language Model (LLM) platforms for Discover.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. The US base salary range for this full-time position is $161,000-$239,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 share 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 benefits at Google. Responsibilities Research and develop state-of-the-art models and strategy to increase overall feed ranking experience.Combine your understanding of product objectives and take advantage of modern Machine Learning (ML) and information retrieval techniques to improve feed ranking quality.Develop Large Language Model (LLM) enhanced Recommendations, integrate Gemini with recommendations, etc.Train on realtime for Tensor Processing Unit (TPU) Training, and Serving efficiency.Advance Discover core feed ranking modeling and ML infrastructure through: Multimodal and cross-domain learning, user behavior sequence modeling, ML-based ranking function modeling, AutoML - Neural Architecture Search and generative recommendation models.