Researcher, Recursive Self-Improvement Safety

The posting, as OpenAI wrote it

ABOUT THE TEAM

Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. Mitigating the frontier risks resulting from these capabilities is paramount to OpenAI’s ability to continue deploying models safely.

The Preparedness team is dedicated to addressing these critical risks. Our work includes:

• Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems.
• Mitigation. Keeping misalignment safeguards, alignment tools, and on track to adequately address extreme threats that might arise in the future.
• 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

Preparedness is hiring strong technical executors to support preparations for accelerated AI development, which may culminate in recursive self-improvement. This work relies on anticipating misalignment risks that might exist in the future, but might not exist now; so it’s especially important that people in this role are tasteful and strategic.

The role is wide-ranging, covering any mitigation for loss of control risk, spanning the design and implementation of better pre-deployment risk-assessment https://openai.com/index/deployment-simulation/, control measures https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment/, RSI-relevant training interventions, and turning one’s technical work into established institutional practices and external-facing communications.

Below is a subset of our focus areas

• Scalable oversight: Establishing practices for model misbehavior monitoring and oversight which remain effective in superhuman model capability regimes, with a focus on bridging from today’s monitoring approaches to future-proof ones.
• Automated auditing: As model capabilities increase, we’ll increasingly rely on automated approaches for finding the most severe forms of model misalignments. We’ll both need to sift through large swaths of production traffic https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment/ to find the most egregious misalignments, and reliably elicit tail risks before deployment.
• Rigorous monitorability: Rigorous testing and red-teaming of our measurements of model misbehavior related to loss-of-control (e.g. reward hacking, sandbagging, scheming). This includes better https://arxiv.org/abs/2603.05706 understanding https://alignment.openai.com/accidental-cot-grading/ monitorability https://arxiv.org/abs/2512.18311, and e.g. preparing for potential losses of Chain-of-Thought monitorability.
• Model behavior science: Design experiments and evaluations to understand the extent to which models are problematically misaligned, or t

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