Machine Learning Engineer, Search and Shopping

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.
• - 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: