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X In most instances, this position requires in-person interviews as part of the hiring process.Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; Pittsburgh, PA, USA.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development, including 5 years of experience with large-scale machine learning, deep learning, neural networks, or recommendation systems.
- Experience designing and implementing large-scale production deep learning or neural network architectures under latency and computational constraints.
- Experience leading cross-functional technical projects and mentoring other engineers.
Preferred qualifications: - PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- Experience with agent-driven ML exploration, hyperparameter tuning, or automated model architecture search.
- Deep expertise in one or more of the following: loss engineering for business objectives, joint modeling across distinct prediction stacks, or hardware-aware ML optimizations (e.g., leveraging dense compute/TPUs effectively).
- Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (e.g., AdBrain, Admixer).
- Demonstrated ability to collaborate with peer technical leads and advanced ML research organizations (such as DeepMind or Google Research) to translate academic or exploratory techniques into production systems.
About the jobIn this role, you will invent novel, low-latency architectures that evaluate layouts in milliseconds while maximizing Tensor Processing Unit capabilities. In close collaboration with DeepMind and Research, you will design sequence modeling to capture deep user history across modern experiences like Artificial Intelligence Overviews and Artificial Intelligence Mode. Additionally, you will engineer loss functions for auction dynamics and deploy agentic artificial intelligence workflows to accelerate model 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: $262000 - $364000 (USD) 25% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead the technical architecture, delivery, and cross-team strategy for Search and Shopping Ads predicted click-through rate (pCTR) models in close partnership with DeepMind, Research, and Ads Machine Learning teams.
- Design, prototype, and scale high-capacity pCTR architectures that maximize modern Tensor Processing Unit (TPU) capabilities while operating within strict low-latency serving and return-on-investment budgets.
- Develop modeling solutions to capture deep user history and nuanced attention signals, seamlessly integrating ads into emerging artificial intelligence Search experiences, including AI Overviews and AI Mode.
- Engineer mathematical loss functions and calibration methods, translating complex business objectives into top-line metric and auction improvements.
- Build agentic machine learning workflows to automate and accelerate optimal model architecture and feature space discovery.
