Researcher, Safety & Privacy

OpenAI OpenAI · AI Frontier · San Francisco, CA · Safety Systems

Researcher focused on designing and building privacy-preserving safety systems for frontier AI models, involving auditable mechanisms for harm detection and mitigation while preserving user data privacy. The role aims to scale automated safety systems to minimize human review and address frontier risks.

What you'd actually do

  1. Design and implement privacy-first architectures for detecting and mitigating harmful model behaviors.
  2. Build frameworks for auditable private identification of high-risk content (jailbreaks, cyber threats, or weaponization instructions).
  3. Develop strict, auditable mechanisms triggered only by harm signals.
  4. Drive the development of automated safety systems that preserve privacy at every level.

Skills

Required

  • PhD or equivalent experience in Computer Science, Cryptography, Security, Machine Learning, or related fields
  • Ability to translate ambiguous problem spaces into formal frameworks and deployable systems
  • Proficiency in privacy-preserving computation (e.g., secure enclaves, MPC, differential privacy)
  • Proficiency in security and adversarial systems
  • Proficiency in machine learning safety or alignment
  • Experience designing robust systems under adversarial threat models
  • Experience with AI safety, jailbreak detection, or model alignment
  • Familiarity with privacy-preserving machine learning techniques, algorithmic auditing and/or secure system design

Nice to have

  • deep interest in privacy, security, and AI safety
  • motivated by building systems that are both trustworthy and effective at scale

What the JD emphasized

  • privacy-preserving safety systems
  • frontier AI models
  • auditable, privacy-first mechanisms
  • harm detection and mitigation
  • scaling automated privacy-preserving safety systems
  • privacy guarantees
  • adversarial conditions
  • privacy-preserving monitoring
  • algorithmic auditing
  • secure enclaves
  • adversarially robust safety enforcement protocols
  • privacy-preserving machine learning techniques
  • algorithmic auditing
  • secure system design

Other signals

  • privacy-preserving safety systems
  • frontier AI models
  • auditable, privacy-first mechanisms
  • harm detection and mitigation
  • scaling automated privacy-preserving safety systems