About this role
ABOUT THE TEAM
Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity.
Our work involves:
1. Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems.
2. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future.
3. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework https://openai.com/index/updating-our-preparedness-framework/, and partnering with other staff to achieve these targets.
This is urgent, fast-paced work that has far-reaching implications for the company and for society.
ABOUT THE ROLE
This role leads the Automated Red Teaming (ART) effort: building scalable, research-driven systems that continuously uncover failure modes in our models and safeguards, and translate those findings into actionable, production-facing improvements. The goal is to reduce expected harm by finding the highest-leverage, least-covered weaknesses early and reliably.
IN THIS ROLE, YOU'LL:
- Own the research and technical direction for automated red teaming across catastrophic risk areas, with an initial emphasis on: - Automated classifier jailbreak discovery (cyber and bio). - Automated bio threat-development elicitation (worst-feasible planning uplift). - CoT monitoring evasion probing (and adjacent loss-of-control evaluations).
- Partner closely with: - Vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and land mitigations. - The Classifiers team to turn discovered attacks into training data, evals, and measurable robustness gains. - Product / Engineering / Safety stakeholders to ensure ART outputs are operationally useful.
YOU MIGHT THRIVE IN THIS ROLE IF YOU:
- Feel a strong pull toward AI safety, and you’re motivated by reducing real-world catastrophic risk (not just publishing cool results).
- Love breaking systems (responsibly) — you get energy from finding weird, high-severity failure modes and turning them into concrete fixes.
- Have strong applied research instincts, especially around evaluations: you’re good at designing experiments that are reproducible, interpretable, and hard to fool.
- Bring hands-on experience with LLMs and agents, including multi-turn behaviors, tool use, and the ways models adapt to constraints.
- Are comfortable building scalable automation, not just prototypes — you can turn red-teaming ideas into pipelines that run continuously and produce high-signal outputs.
- Have solid software engineering fundamentals (data structures, algorithms, testing discipline) and you can work effectively in a production-adjacent environment.
- Think in threat models and incentives, and