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Meta Platforms · San Francisco, CA

# AI Research Scientist, FAIR Security, Privacy, and Reliability

San Francisco, CA Research & Science 

Listed on Meta Platforms’s official careers site. Listed as of October 18, 2026. Check that it is still open and apply there.
[Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/1414134214191432/)

[OpenNash Jobs](https://jobs.opennash.com/) / [Meta Platforms](https://jobs.opennash.com/companies/meta-platforms/) / AI Research Scientist, FAIR Security, Privacy, and Reliability

## Job description

The FAIR Security, Privacy, and Reliability team breaks and fixes agents and the foundational models that power them. We conduct fundamental research to discover novel attacks, measure privacy, and find non-intuitive breaks in robustness. We frequently contribute those benchmarks to Meta's model Evaluation Reports and have published many of them in top research venues, earning top-1% recognition at ICLR and ICML. The team also lands novel post-training mitigations for these risks in both the open-source line of models and has multiple opportunities for direct research-to-production.

## Responsibilities

Conduct fundamental research to discover novel safety and security failures, robust approaches to measuring memorization risk or reliability failures in AI agents and foundational models, Develop and contribute benchmarks to Meta's foundational model evaluation suite and/or the Evaluation Reports Design and implement novel post-training mitigations for safety, security, privacy, and reliability risks in foundation models Collaborate with cross-functional teams to translate research into production systems

## Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Computer Science, Machine Learning, or related field, or equivalent practical experience
- Research experience in at least one of the following areas: safety, security, privacy, or robustness of AI models; adversarial machine learning; indirect prompt injections or jailbreaks; contextual integrity or memorization; reward hacking or other agent reliability failures; testing or mitigating foundation models for catastrophic risk (CBRNE, cyber, loss of control); or developing mitigations in any of these areas
- Track record of publications in top-tier research venues

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 11, 2026
- Last checked: Oct 18, 2026
- Listed for: 7 days
- Location: San Francisco, CA[Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/1414134214191432/)

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