Researcher, Misalignment Monitoring Lead

About the Team

OpenAI's Safety Research organization works to ensure that our most capable AI models can be deployed safely and responsibly. As frontier models become increasingly capable and autonomous, developing reliable ways to monitor their behavior, identify emerging risks, and evaluate the effectiveness of safety systems becomes increasingly important.

Our work focuses on advancing the research and evaluation methods needed to understand model behavior and ensure that monitoring systems remain effective as model capabilities evolve. We combine rigorous empirical research with practical safety interventions, working closely with research, engineering, and deployment teams to translate findings into improvements across OpenAI's models and products.

About the Role

We are looking for a senior researcher to lead technical work on misalignment monitoring for frontier AI systems. You will develop methods to evaluate the effectiveness of AI monitors, understand their limitations, and improve our ability to detect and stop concerning model behavior reliably.

This role combines research leadership with hands-on technical execution. You will set research priorities, design rigorous evaluations, investigate monitoring failures, and develop approaches for stress-testing and improving monitoring systems. A key focus will be understanding where monitors produce false positives, miss important behaviors, or become less effective as models grow more capable.

You will work closely with researchers and engineers across OpenAI to turn monitoring research into practical improvements to our safety systems. We are looking for someone with strong research judgment, technical depth, and the ability to independently drive ambiguous problems from initial investigation through measurable results.

In this role, you will

• Lead the research and technical direction for evaluating and improving the misalignment monitoring of frontier AI systems.
• Design and execute rigorous evaluations that measure monitor effectiveness, identify failure modes, and assess the reliability of monitoring signals.
• Develop methods to stress-test and red-team monitors, including identifying behaviors that evade detection or expose weaknesses in existing approaches.
• Investigate false positives, missed detections, and other monitoring failures to improve the accuracy and usefulness of safety evaluations.
• Build scalable evaluation frameworks and experimental tools that enable ongoing measurement of monitoring performance as model capabilities evolve.
• Translate research findings into actionable improvements, working with partner teams to strengthen monitoring systems and inform broader safety decisions.
• Establish research priorities and guide technical execution, balancing foundational research questions with practical deployment needs.

You might thrive in this role if you

• Have a strong background in machine learning research, particularly in model evaluations, AI safety, alignment, robustness, or related areas.
• Have demonstrated the ability to lead technically challenging research projects, from identifying important questions through designing experiments and delivering results.
• Bring strong experimental judgment and understand how to design evaluations that are rigorous, reproducible, and difficult to game.
• Have hands-on experience building or evaluating machine learning systems, including working with large language models or other frontier AI systems.
• Enjoy investigating complex failure modes and developing methods to distinguish meaningful safety signals from misleading measurements.
• Are comfortable working on open-ended research problems where the right methodology or success criteria may not be obvious at the outset.
• Combine strong research instincts with practical engineering ability and can independently build experiments, analyze results, and iterate quickly.
• Communicate technical findings clearly and enjoy collaborating across research, engineering, and safety teams.
• Are motivated 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 that are used in real-world deployments.
• A track record of technical research leadership, including mentoring researchers or coordinating cross-functional research initiatives.

About OpenAI

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.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement .

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates.

For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information.

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