OpenAI의 Preparedness 팀에서 재귀적 자기 개선(RSI) 관련 위험을 완화할 연구자를 채용합니다. AI 안전성 및 정렬(Alignment) 분야의 뛰어난 기술적 실행력과 전략적 통찰력이 필수입니다. 미래의 잠재적 위협을 예측하고, 이를 방지하기 위한 자동화된 감사 및 확장 가능한 감독 체계를 구축하는 것이 핵심 과제입니다. 급변하는 환경에서 빠르게 프로토타입을 설계하고 실질적인 안전 파이프라인을 구현할 인재를 찾습니다.
Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats that could scale to an extreme level of severity.
Our work involves:
Tracking and prediction. Monitoring and predicting the evolving misalignment propensities and 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.
Preparedness is hiring strong technical executors to support preparations for recursive self-improvement. This work relies on reasoning about problems 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, control measures, RSI-relevant training interventions, and turning one’s technical work into established institutional practices.
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 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 understanding monitorability, 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 their safety-relevant capabilities lag behind dangerous capabilities. This may include training model organisms of misbehavior for behaviors not currently present in production, or training interventions to increase safety-relevant capabilities.
Coordination and verification: Prototype technical mechanisms for verifying compliance with potential future AI safety agreements.
AI R&D risk measurement: Track progress toward automation of technical staff to inform OpenAI’s near-term investments in alignment and security.
Maintaining and strengthening RSI safety cases: We’re especially interested in identifying and addressing blindspots of mitigation areas which we may have missed.
Generally, our team alternates between performing rigorous hypothesis-driven research and turning our insights into interventions or control systems which impact production models, with occasional support of engineering teams.
Carefully consider the problems OpenAI might face in the future and how to prepare for them.
Turn an open-ended objective like “prepare for future security threats” into a much more concrete direction (e.g. “implement monitors for data poisoning”) – prioritizing the work that is most useful to start right now.
Execute quickly, building scrappy prototypes, and then improving them iteratively until they become established components of our safety pipelines.
Secure buy-in from other staff at OpenAI when necessary, and communicate your work clearly.
Collaborate with or manage other staff as needed, since we might need to rapidly scale to tackle these problems quickly.
Are an exceptional technical executor.
Have strong strategic and research taste: you can prioritize effectively in domains with weak feedback loops.
Are passionate about mitigating the risks associated with recursive self-improvement.
Are driven by a desire to do whatever work most positively impacts the future of AI development.
Bonus: you have already done work in one of the domains listed above (ML research, AI alignment, AI verification etc).
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