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AI Model Weight Tampering Detection Researcher

Aria Watanabe

Full-time · Job · San_Francisco

About the role

We publish internally and encourage conference submissions. You'll be expected to read papers and implement findings that push our alignment and safety research forward. As models get deployed across more sensitive infrastructure, we've become increasingly concerned about a threat model our team hasn't formally addressed: subtle tampering with model weights post-training that changes behavior in narrow, hard-to-detect ways. You'll lead research into detection methods for this class of attack, from statistical fingerprinting to behavioral probing.

Responsibilities

  • Research and develop detection methods for post-training model weight tampering
  • Design statistical fingerprinting techniques for model weight integrity
  • Publish findings internally and at relevant safety conferences
  • Collaborate with the interpretability and red team research groups

Requirements

  • 5+ years of research experience in ML security, safety, or adversarial robustness
  • Deep PyTorch and TensorFlow experience
  • Strong statistical background for fingerprinting and anomaly detection methods
  • Publication record in relevant ML safety or security venues preferred

Benefits

  • Fully remote position
  • Top-of-market compensation and equity
  • Health, dental, and vision coverage
  • Full conference travel and publication support
  • Dedicated research time allocation

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch tensorflow statistics aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$200k+ / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch tensorflow statistics aws

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