Machine Learning Engineer, Search and Shopping

In most instances, this position requires in‑person interviews as part of the hiring process. Applicants can share their preferred working location: 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.
• 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 next‑generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products handle information at massive scale and extend well beyond web search, spanning information retrieval, distributed computing, large‑scale system design, networking, data storage, security, artificial intelligence, natural language processing, UI design, mobile, and more. As a software engineer you will work on a critical project with opportunities to switch teams as the business evolves. You will invent novel, low‑latency architectures that evaluate layouts in milliseconds while maximizing Tensor Processing Unit capabilities, design sequence modeling to capture deep user history across modern experiences, engineer loss functions for auction dynamics, and deploy agentic AI workflows to accelerate model discovery. Google Ads leverages cutting‑edge machine learning and Generative AI models like Gemini to power a multi‑billion‑dollar global business, directly impacting billions of users.

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.

Pay

US: $207,000 – $300,000 (USD) + 20% bonus target + equity + benefits.