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OpenAI's Safety Research organization is seeking a senior researcher to lead technical work on misalignment monitoring for frontier AI systems. This role combines research leadership with hands-on technical execution, focusing on developing methods to evaluate the effectiveness of AI monitors, understand their limitations, and improve the ability to detect and stop concerning model behavior reliably.
You will set research priorities, design rigorous evaluations, investigate monitoring failures, and develop approaches for stress-testing and improving monitoring systems. Key responsibilities include:
- Leading research and technical direction for evaluating and improving misalignment monitoring of frontier AI systems
- Designing and executing rigorous evaluations that measure monitor effectiveness, identify failure modes, and assess reliability of monitoring signals
- Developing methods to stress-test and red-team monitors, identifying behaviors that evade detection or expose weaknesses
- Investigating false positives, missed detections, and other monitoring failures to improve accuracy and usefulness of safety evaluations
- Building scalable evaluation frameworks and experimental tools for ongoing measurement of monitoring performance as model capabilities evolve
- Translating research findings into actionable improvements, working with partner teams to strengthen monitoring systems
- Establishing research priorities and guiding technical execution, balancing foundational research with practical deployment needs
You will work closely with researchers and engineers across OpenAI to turn monitoring research into practical improvements to safety systems. The role requires strong research judgment, technical depth, and the ability to independently drive ambiguous problems from initial investigation through measurable results.
REQUIREMENTS:
- Strong background in machine learning research, particularly in model evaluations, AI safety, alignment, robustness, or related areas
- Demonstrated ability to lead technically challenging research projects, from identifying important questions through designing experiments and delivering results
- Strong experimental judgment and understanding of how to design evaluations that are rigorous, reproducible, and difficult to game
- Hands-on experience building or evaluating machine learning systems, including work with large language models or other frontier AI systems
- Ability to investigate complex failure modes and develop methods to distinguish meaningful safety signals from misleading measurements
- Comfort working on open-ended research problems where the right methodology or success criteria may not be obvious at the outset
- Strong research instincts combined with practical engineering ability; ability to independently build experiments, analyze results, and iterate quickly
- Clear communication of technical findings and enjoyment of collaborating across research, engineering, and safety teams
- Motivation by OpenAI's mission and the challenge of developing reliable oversight mechanisms for increasingly capable AI systems
NICE TO HAVE:
- Experience with AI monitoring, automated evaluations, adversarial testing, or red-teaming
- Research experience in scalable oversight, AI control, model behavior analysis, or interpretability
- Experience developing monitoring or evaluation systems used in real-world deployments
- Track record of technical research leadership, including mentoring researchers or coordinating cross-functional research initiatives
