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security ai safety evaluation infrastructure system-level testing adversarial mlπ Description
- Design and implement worst-case demonstrations that reveal AGI alignment risks.
- Develop adversarial and system-level evaluations grounded in those demonstrations.
- Create automated tools and infrastructure to scale automated red-teaming and stress testing.
- Research failure modes of alignment techniques and propose improvements.
- Publish papers that shift safety strategy or industry practice.
- Partner with engineering, research, policy, and legal teams to integrate findings into safeguards and governance processes.
π― Requirements
- 4+ years in AI red-teaming, security research, adversarial ML, or related safety fields.
- Strong research track record: publications, open-source projects, or high-impact internal work.
- Fluent in modern ML/AI techniques; comfortable hacking on large-scale codebases and eval infra.
- Clear communication with technical and non-technical audiences; translate findings into actionable recommendations.
- Ability to drive cross-functional projects spanning research, engineering, and policy.
- PhD or masters in CS/ML/security or equivalent (nice to have but not required).
π Benefits
- Equal opportunity employer; diverse perspectives welcomed.
- Reasonable accommodations for applicants with disabilities.
- OpenAI privacy policy and data protections.
- Background checks and compliance with applicable laws.
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