Researcher, Automated Red Teaming

About the team The Safety Systems org ensures that OpenAI’s most capable models can be responsibly developed and deployed. We build evaluations, safeguards, and safety frameworks that help our models behave as intended in real-world settings.The Preparedness team is an important part of the Safety Systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework.Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks. To ensure that AI promotes positive change, the Preparedness team helps us prepare for the development of increasingly capable frontier AI models. This team is tasked with identifying, tracking, and preparing for catastrophic risks related to frontier AI models.The mission of the Preparedness team is to:Closely monitor and predict the evolving capabilities of frontier AI systems, with an eye towards risks whose impact could be catastrophicEnsure we have concrete procedures, infrastructure and partnerships to mitigate these risks and to safely handle the development of powerful AI systemsPreparedness tightly connects capability assessment, evaluations, and internal red teaming, and mitigations for frontier models, as well as overall coordination on AGI preparedness. This is fast paced, exciting work that has far reaching importance for the company and for society.About the roleThis role leads the Automated Red Teaming (ART) effort: building scalable, research-driven systems that continuously discover failure modes in our models and mitigations — and translate those findings into actionable, production-facing improvements. The goal is to maximize counterfactual reduction in expected harm by finding the highest-leverage, least-covered weaknesses early and reliably.In this role you willYou will 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)You will partner tightly with:Vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and land mitigationsThe Classifiers team to turn discovered attacks into training data, evals, and measurable robustness gainsProduct / eng / safety stakeholders to ensure ART outputs are operationally useful (not just interesting)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 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.About OpenAIOpenAI is an ... (truncated, view full listing at source)