Machine Learning Engineer, Search and Shopping

Google is hiring a Machine Learning Engineer to help power Search and Shopping Ads. The role centers on building and scaling low-latency predicted click-through rate (pCTR) models, then integrating ads into emerging AI search experiences while meeting tight compute and return-on-investment constraints.

Based in Mountain View, CA with onsite work, you will partner closely across Ads Machine Learning and research groups, including DeepMind, to design architectures and training methods that translate user signals and auction objectives into production improvements.

Role focus

• Lead the technical architecture, delivery, and cross-team strategy for Search and Shopping Ads pCTR models in partnership with DeepMind, Research, and Ads Machine Learning teams.
• Design, prototype, and scale high-capacity pCTR architectures that maximize Tensor Processing Unit (TPU) capabilities while operating under strict low-latency serving and return-on-investment budgets.
• Develop modeling solutions that capture deep user history and nuanced attention signals, integrating ads into emerging AI Search experiences such as AI Overviews and AI Mode.
• Engineer loss functions and calibration methods to convert complex business and auction objectives into measurable metric and auction improvements.
• Build agentic machine learning workflows that automate and accelerate optimal model architecture and feature space discovery.

About the job

• Invent novel low-latency architectures that evaluate layouts in milliseconds while maximizing TPU capabilities.
• In close collaboration with DeepMind and Research, design sequence modeling to capture deep user history across modern experiences like Artificial Intelligence Overviews and Artificial Intelligence Mode.
• Engineer loss functions for auction dynamics and deploy agentic AI workflows to accelerate model discovery.

Required qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development, including 5 years 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 related fields.
• Experience with agent-driven ML exploration, hyperparameter tuning, or automated model architecture search.
• Experience with one or more of: loss engineering for business objectives, joint modeling across distinct prediction stacks, or hardware-aware ML optimizations (for example, leveraging dense compute/TPUs effectively).
• Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (such as AdBrain or Admixer).
• Ability to collaborate with peer technical leads and advanced ML research organizations (such as DeepMind or Google Research) to bring academic or exploratory work into production.

Technologies

• Tensor Processing Unit (TPU)
• Large-scale machine learning, deep learning, neural networks
• Recommendation systems
• Sequence modeling
• Agentic artificial intelligence workflows / agentic machine learning workflows
• Loss functions, calibration methods

Compensation and benefits

• US salary range: $207,000 - $300,000 (USD) per year
• 20% bonus target + equity + benefits
• Learn more about benefits at Google.