Minimum qualifications: Bachelor’s degree or equivalent practical experience. 2 years of experience in software development (e.g., C++, Python). 2 years of experience in testing, maintaining, or launching software products. Experience building, training, and deploying machine learning models using TensorFlow, JAX, or Adbrain. Experience working with ranking, retrieval and other recommendation systems models. Preferred qualifications: Master's degree or PhD in Computer Science or related technical fields. 2 years of experience with data structures and algorithms. Experience with generative AI techniques (e.g., LLMs, natural language processing) and integrating them into production systems. Excellent investigative and quantitative reasoning skills, with a foundation in statistics and experiment design (A/B testing). Proven track record of managing large-scale ML systems, conducting analysis of quality systems, and identifying bottlenecks to improve performance. 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 be at the forefront of integrating highly relevant travel ads into AI Overviews/AI Mode and web search experiences. You will bridge the gap between generative AI and core ads infrastructure. You will build and optimize the deep learning models powering ads ranking and retrieval alongside integrating a Large Language Model (LLMs). You will leverage user intent and contextual signals to deliver ads that feel like a natural, helpful extension of the user's travel planning journey. This is a unique opportunity to apply your expertise in recommendation/search system and deep learning technology (Adbrain, TensorFlow, JAX) to scale features, drive significant business impact, and shape the future of travel discovery. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Build, train, and scale deep learning models for ranking, retrieval and generation use cases using Adbrain, TensorFlow, or JAX, alongside efficient GenAI inference integration. Own the end-to-end design implementation, and deployment of ML features and data pipelines across AI surfaces, ensuring high code quality and system performance. Design, launch, and analyze A/B experiments to evaluate model performance, monitor user engagement, and drive improvements in ad relevance and business. Work closely with immediate teammates and cross-functional partners (Product, Data Science, UX) to clarify requirements and resolve technical blockers.