Machine Learning Engineer, Search and Shopping

• 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.

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.
• Experience 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).
• 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.

• 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.