Machine Learning Engineer, AI/ML Expert for Agents and TrustGraph, Google Ads

Google•Mountain View, CA

4d•$147,000 - $210,000

About The Position

Google is seeking a software engineer for the Agents and TrustGraph (ATG) team, a cross-cutting organization spanning Ads Privacy and Safety, Consumer Trust and Safety, and partner product areas. The team builds foundational AI platforms, relational intelligence infrastructure, and adversarial crawling systems to protect users, advertisers, and publishers from bad actors. Its engineers may work across projects and shift to areas with the greatest impact. Within Google Ads, the role applies machine learning and Generative AI to protect users, improve ad quality, and optimize advertiser campaigns. The work spans Generative AI, distributed graph systems, and adversarial security and anti-abuse, with opportunities to collaborate with Google Research, DeepMind, and other teams. The position contributes to systems and products operating at global scale.

Requirements

Bachelor’s degree or equivalent practical experience.
Two years of experience with software development in Python or another programming language, or one year of experience with an advanced degree.
Two years of experience with distributed systems.
One year of experience with end-to-end machine learning, such as model deployment, model evaluation, data processing, or debugging.
Experience with Generative AI techniques, such as LLMs, multimodal models, or large vision models, or with related concepts such as language modeling or computer vision.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
Two years of experience with data structures and algorithms.
Experience developing accessible technologies.
Experience working with large-scale datasets, graph systems, web or app infrastructure, or Generative AI/LLM-based systems.
Experience building LLM agents, multi-agent orchestration frameworks, automated red-teaming or adversarial simulation systems, or automated evaluation pipelines.
Experience with large-scale graph databases or algorithms, such as Spanner, entity resolution, clustering, or graph mining, or with web/app crawling and anti-cloaking technologies.

Responsibilities

Contribute to existing documentation or educational content, adapting it based on product or program updates and user feedback.
Implement Generative AI solutions, use machine learning infrastructure, and contribute to data preparation, optimization, and performance enhancements.
Build autonomous red-teaming and adversarial simulation frameworks to probe web and app crawling infrastructure, identify cloaking and evasion blind spots, and help harden production crawlers.
Design high-throughput graph pipelines, entity-resolution and expansion algorithms, semantic layers, and LLM-powered graph reasoning tools for anti-abuse intelligence.
Write product or system development code and contribute across projects at the intersection of Generative AI, distributed graph systems, and adversarial security and anti-abuse.