OpenAI의 Preparedness 팀에서 Automated Red Teaming(ART) 연구를 주도할 인재를 찾습니다. LLM 및 AI 안전 분야의 전문성을 바탕으로 모델의 취약점을 자동 탐지하고, 이를 실질적인 보안 개선으로 연결하는 핵심 역할을 수행합니다. 확장 가능한 자동화 시스템 구축 경험과 적대적 머신러닝에 대한 깊은 이해가 필수적입니다.
Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security that could scale to an extreme level of severity.
Our work involves:
Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems.
Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future.
Coordination. Setting mitigation targets by maintaining OpenAI’s 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.
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.
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.
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 you naturally ask “what would an attacker do next?” or “how would this fail under pressure?”
Can translate messy findings into action, communicating clearly with researchers, engineers, product, and policy — and driving alignment on what to fix first.
Care about efficiency and prioritization, and you’re happy to say “no” to low-leverage work to focus on what moves the risk needle.
Nice to have:
Experience in adversarial ML, security research / red teaming, abuse prevention systems, or large-scale eval infrastructure.
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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