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

Google
California, USA
Junior · 2+ years experience
Onsite
USD 147000-210000 / year
Discovered 3 days ago
python

Full Job Posting

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.

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.

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