Machine Learning Engineer, Search and Shopping

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.

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.

In 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 at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% 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.