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

About The Job

Google's software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Engineers are needed 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 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. ATG builds foundational AI platforms, relational intelligence infrastructure, and adversarial crawling systems that protect Google’s users, advertisers, and publishers from bad actors.

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. This work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. The team fosters a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex issues.

Key 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.

Requirements & Tech Stack

• Python or more programming languages
• Distributed systems
• End-to-end machine learning (e.g., model deployment, model evaluation, data processing, debugging)
• GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (e.g., language modeling, computer vision)
• Data structures and algorithms
• Accessible technologies development
• Large-scale datasets, graph systems, web/app infrastructure, or Generative AI/LLM-based systems
• Building LLM agents, multi-agent orchestration frameworks, automated red-teaming/adversarial simulation systems, or automated evaluation pipelines
• Large-scale graph databases/algorithms (e.g., Spanner, entity resolution, clustering, graph mining) or web/app crawling and anti-cloaking technologies

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: Master's degree or PhD in Computer Science or related technical fields.
• Preferred: 2 years of experience with data structures and algorithms.
• Preferred: Experience developing accessible technologies.
• Preferred: Experience working with large-scale datasets, graph systems, web/app infrastructure, or Generative AI/LLM-based systems.
• Preferred: Experience building LLM agents, multi-agent orchestration frameworks, automated red-teaming/adversarial simulation systems, or automated evaluation pipelines.
• Preferred: Experience with large-scale graph databases/algorithms (e.g., Spanner, entity resolution, clustering, graph mining) or web/app crawling and anti-cloaking technologies.