Minimum qualifications: Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products. 5 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture. 3 years of experience with software design and architecture. Experience designing or deploying machine learning (ML) infrastructure or platforms. Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. Proficiency in deep learning frameworks (e.g., TensorFlow, PyTorch) and cloud/distributed data technologies. 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. YouTube Ads is a multi-billion dollar business and the fastest growing segment of Google's Ads. Our mission is to power the YouTube ecosystem by keeping it free and engaging for users, responsibility rewarding creators and connecting businesses to their unique audiences. The YouTube Ads Creative Optimization (YACO) team drives the mission to effectively connect businesses and users with the best creative technology. We specialize in Creative Automation applications that drive advertiser value with minimal advertiser effort through leveraging the AI technologies. This team uniquely grows at the intersection of Ads, YouTube and Research. 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 Serve as the technical leader responsible for architecting, scaling, and steering the next-generation infrastructure that powers our AI/ML applications. You will operate at the intersection of Ads, YouTube, and GenAI research to build high-throughput systems that enable seamless creative generation and optimization. Define the technical roadmap and architect scalable foundational infrastructure, including creative data engines, generation and rendering pipelines, and agentic orchestration frameworks. Design and optimize distributed systems to manage complex GenAI and heavy media-processing workloads efficiently. Build and mature experiment and learning infrastructure to accelerate model training, evaluation, and rapid deployment. Partner with Product Management, Research, and executive leadership to align infrastructure capabilities with long-term business goals. Guide, mentor, and elevate executive and junior engineers on the team, fostering a culture of technical excellence and execution.