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OpenAIOpenAISan Francisco, CA

Researcher, Safety & Privacy

Designs privacy-preserving safety systems for frontier AI models, focusing on auditable harm detection (jailbreaks, cyber threats, bioweapons) without exposing user data. Requires PhD in CS/crypto/ML/security and expertise in privacy tech like MPC and differential privacy.

295k – 445k/yr
On-siteOther

About the role

Responsibilities

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

Requirements

  • Deep interest in privacy, security, and AI safety, motivated by building systems that are both trustworthy and effective at scale.
  • 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 one or more of:
    • Privacy-preserving computation (e.g., secure enclaves, MPC, differential privacy)
    • Security and adversarial systems
    • 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.

Skills

Differential PrivacySecure EnclavesMpcMachine Learning SafetyAi AlignmentJailbreak DetectionPrivacy-Preserving MlAlgorithmic AuditingAdversarial Threat ModelsCryptography

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