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

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 2 years of experience with software development in Python or more programming

languages, or 1 year of experience with an advanced degree.

• 2 years experience with distributed systems.
• 1 year of experience with end-to-end machine learning (e.g., model

deployment, model evaluation, data processing, debugging).

• Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision

Models) or with GenAI-related concepts (e.g., language modeling, computer

vision).

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in Computer Science or related technical fields.

• 2 years of experience with data structures and algorithms.
• Experience developing accessible technologies.

• Experience working with large-scale datasets, graph systems, web/app

infrastructure, or Generative AI/LLM-based systems.

• Experience building LLM agents, multi-agent orchestration frameworks,

automated red-teaming/adversarial simulation systems, or automated evaluation

pipelines.

• Experience with large-scale graph databases/algorithms (e.g., Spanner, entity

resolution, clustering, graph mining) or web/app crawling and anti-cloaking

technologies.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. We're looking for engineers who bring fresh ideas from

all areas, including information retrieval, distributed computing, large-scale

system design, networking and data storage, security, artificial intelligence,

natural language processing, UI design and mobile; the list goes on and is

growing every day. As a software engineer, you will work on a specific project

critical to Google’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. We need our engineers to be

versatile, display leadership qualities and be enthusiastic to take on new

problems across the full-stack as we continue to push technology forward.

The Agents and TrustGraph (ATG) team is a broad, cross-cutting horizontal

organization spanning Ads Privacy and Safety, Consumer Trust and Safety, and

partner Product Areas across Google. We build the foundational AI platforms,

relational intelligence infrastructure, and adversarial crawling systems that

protect Google’s users, advertisers, and publishers from bad actors.

As online abuse and fraud grow in volume and sophistication—increasingly powered

by generative AI—static rules and reactive manual workflows are no longer

enough. Because ATG operates as a horizontal pillar with Google-wide fan-out,

engineers on our team are not siloed into a single narrow product. Instead, you

will have the flexibility to work across projects and pivot to where the highest

leverage lies.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine

learning and Generative AI models like Gemini to power a multi-billion dollar

global business.

Our work directly impacts billions of users by protecting users from harm,

improving ad quality, and optimizing campaigns for advertiser

return-on-investment. We foster a culture of deep collaboration, partnering

closely with teams like Google Research and DeepMind to solve complex issues.

Join us to work on AI, take on problems at an unparalleled scale, and build the

next generation of advertising technology.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Contribute to existing documentation or educational content, adapt content

based on product/program updates and user feedback.

• Implement GenAI solutions, utilize ML infrastructure, and contribute to data

preparation, optimization,  performance enhancements.

• Build autonomous red-teaming and adversarial simulation frameworks to

proactively probe web/app crawling infrastructure, uncover cloaking and

evasion blind spots, and close the loop to harden production crawlers.

• Design high-throughput graph pipelines, entity-resolution and expansion

algorithms, semantic layers, and LLM-powered graph reasoning tools that make

anti-abuse intelligence accessible across dozens of teams.

• Write product or system development code. Work at the intersection of

Generative AI, Distributed Graph Systems, and Adversarial

Security/Anti-Abuse, with the flexibility to contribute across multiple

projects.