Minimum qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing and launching software products, and 3 years of experience with software design and architecture. Preferred qualifications: Master's degree or PhD in Computer Science, Machine Learning, or a related technical field. 8 years of experience in data structures and algorithms. 5 years of experience building and productionizing machine learning models, with experience in deep learning or reinforcement learning. Experience with machine learning frameworks like TensorFlow and JAX, production machine learning platforms such as TensorFlow Extended and AdBrain, and generative models and their applications. Proficiency in designing, running, and analyzing large-scale online experiments (A/B tests). Familiarity with online advertising systems, creative optimization, personalization, or recommender systems. 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. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. In this role, you will focus on revolutionizing how ad creatives are composed, personalized, and optimized at scale using artificial intelligence/machine learning techniques. You will develop intelligent systems that enhance the relevance and performance of Ad creatives served to billions of YouTube users. Your work spans deep learning, reinforcement learning, generative artificial intelligence, and large-scale serving platforms to power the next generation of ad creative experiences. 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: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Lead the end-to-end development of novel machine learning models, incorporating techniques like deep learning, reinforcement learning, and generative artificial intelligence, from concept to production. Build and scale end-to-end machine earning pipelines for model training, inference, and integration with high-throughput Ad serving systems. Explore, implement, and integrate with generative models for text, image, and video adaptations within Ads. Apply deep learning and reinforcement learning to understand asset value and optimize creative composition while developing metrics and algorithms to ensure creative freshness and efficient exploration. Contribute to the architecture of centralized services for unifying asset attributes and model-driven insights across different applications, collaborating with infrastructure and serving teams to power creative optimization.