Senior ML Engineer - Large-Scale Search & Ads (TPU)

Google Inc.; Northern (KY); Hybrid; USD 207,000 - 300,000; Full time

Job summary

Google is seeking a Machine Learning Engineer for Search and Shopping who will drive production ML systems at scale. You will work across teams to design low-latency architectures and maximize TPU utilization, translating research into practical, scalable solutions for ads and search experiences.

The role demands strong background in large-scale ML, deep learning, and cross-functional leadership, with opportunities to collaborate with DeepMind and Google Research on advanced techniques.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of software development, including 5 years in large-scale ML, deep learning, neural networks, or recommender systems.
• Experience designing and implementing large-scale production DL architectures under latency constraints.

Responsibilities

• Lead technical architecture and cross-team strategy for Search and Shopping pCTR models with DeepMind/Ads ML teams.
• Design, prototype, and scale high-capacity pCTR architectures maximizing TPU capabilities under low-latency constraints.
• Develop modeling to capture deep user history and attention signals for AI-driven search experiences.
• Engineer loss functions and calibration methods to improve business objectives and ROIs.
• Build agentic ML workflows to accelerate model architecture and feature space discovery.

Skills

Software development

Large-scale ML

Mentoring engineers

Education

Bachelor’s degree or equivalent practical experience

PhD (preferred) in CS/ML/AI

Tools

TPU

TensorFlow

Job description

Google is seeking a Machine Learning Engineer for Search and Shopping who will drive production ML systems at scale. You will work across teams to design low-latency architectures and maximize TPU utilization, translating research into practical, scalable solutions for ads and search experiences.

The role demands strong background in large-scale ML, deep learning, and cross-functional leadership, with opportunities to collaborate with DeepMind and Google Research on advanced techniques.