Loss Function Developer Jobs

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Machine Learning Engineer, Search and Shopping

Google Mountain View, CA

Mountain View, CA

Today

$207k - $300k/yr Full time Ad Tech Agentic Ai Ai Agent

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Job Description

Build low-latency machine learning for Search and Shopping Ads at Google from Mountain View, CA (onsite). This role focuses on creating and scaling predicted click-through rate (pCTR) models that are designed to run within tight serving and return-on-investment budgets, while integrating ads into emerging AI-driven search experiences. You will work closely with DeepMind, Research, and Ads Machine Learning teams to translate modeling advances into production systems.

What you’ll do

Own technical architecture and cross-team strategy for Search and Shopping Ads pCTR modeling, partnering closely with DeepMind, Research, and Ads Machine Learning.
Design, prototype, and scale high-capacity pCTR architectures that maximize modern Tensor Processing Unit (TPU) performance while meeting strict low-latency serving and ROI constraints.
Develop modeling approaches 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 that connect complex business objectives to measurable top-line and auction improvements.
Build agentic machine learning workflows to automate and accelerate model architecture and feature space discovery.

What you bring

Bachelor’s degree or equivalent practical experience.
8 years of experience with software development, including 5 years in 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.

Helpful experience

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 with one or more areas such as loss engineering for business objectives, joint modeling across distinct prediction stacks, or hardware-aware ML optimizations (for example, leveraging dense compute and TPUs effectively).
Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (such as AdBrain, Admixer).
Ability to collaborate with peer technical leads and advanced ML research organizations (for example, DeepMind or Google Research) to bring academic and exploratory techniques into production.

Tech you’ll work with

Tensor Processing Unit (TPU), large-scale machine learning, deep learning, neural networks, and recommendation systems
Sequence modeling and agentic artificial intelligence workflows
Agentic machine learning workflows, loss functions, and calibration methods

Compensation and benefits

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

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