Calibration Methods Engineer 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 and scale low-latency machine learning for Search and Shopping Ads, optimizing predicted click-through rate (pCTR) models and ad integration in emerging AI search experiences.

Responsibilities

Own technical architecture, delivery, and cross-team strategy for Search and Shopping Ads pCTR models in close partnership with DeepMind, Research, and Ads Machine Learning teams.
Design, prototype, and scale high-capacity pCTR architectures that maximize Tensor Processing Unit (TPU) performance while meeting 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 including AI Overviews and AI Mode.
Engineer mathematical loss functions and calibration methods to translate business objectives into improvements across top-line metrics and auction outcomes.
Build agentic machine learning workflows to automate and accelerate optimal model architecture and feature space discovery.

Requirements

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 a related field.
Experience with agent-driven ML exploration, hyperparameter tuning, or automated model architecture search.
Experience with one or more of the following:
- Loss engineering for business objectives
- Joint modeling across distinct prediction stacks
- Hardware-aware ML optimizations (example: leveraging dense compute/TPUs effectively)
Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (example: 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 methods into production systems.

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 Location

Location: Mountain View, CA (onsite)
Salary: USD 207,000 - 300,000 per year
US compensation package: $207,000 - $300,000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.

About the Job

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

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