About the job
We are seeking a Researcher in Privacy-Preserving Safety to help design and build the next generation of privacy-preserving safety systems for frontier AI models. This role sits at the intersection of AI safety, security, and privacy, with a focus on developing auditable, privacy-first mechanisms that enable robust harm detection and mitigation without exposing sensitive user data.
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.
Qualifications
Minimum
Hold a PhD or equivalent experience in Computer Science, Cryptography, Security, Machine Learning, or related fields
Preferred
Have the ability to translate ambiguous problem spaces into formal frameworks and deployable systems
Demonstrate 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
Have experience with AI safety, jailbreak detection, or model alignment
Are familiar with privacy-preserving machine learning techniques, algorithmic auditing and/or secure system design