Red Team Specialist - Cyber

About the Team The Intelligence and Investigations team is dedicated to ensuring the safe, responsible deployment of AI by rapidly detecting and mitigating abuse. Our team leverages the latest testing methodologies to uncover vulnerabilities and emerging threats, helping safeguard OpenAI’s products and users. We work closely with cross-functional partners across product, policy, and engineering to drive a comprehensive defense strategy against evolving adversarial challenges. About the Role As a Red Team Specialist focused on cyber, you will help answer two practical questions: What cyber capabilities can our models provide to real-world attackers, and do our safeguards remain effective when those attackers use increasingly sophisticated techniques? The role combines scaled evaluation with expert-driven testing. You may bring deeper experience in cybersecurity and use that expertise to judge whether a model’s behavior meaningfully changes attacker capability. Alternatively, you may bring deeper experience in model evaluations, automation, or agentic harnesses and apply those skills to building rigorous cyber testing. We do not expect every candidate to be equally deep in both areas, but successful candidates will have a strong foundation in one and enough fluency in the other to work effectively across the boundary. Most of your work will focus on model cyber capabilities and safeguards; you will also spend a portion of your time testing novel abuse risks in agentic systems. This role is located in San Francisco, CA or Seattle, WA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design and run rigorous evaluations of model cyber capabilities and safeguards, including policy adherence, correct refusal, over refusal, and resilience to jailbreaking and other adversarial techniques. - Conduct hands-on testing to understand what models can enable when used by experienced security practitioners, including through task-specific harnesses, scaffolding, and multi-step workflows. - Distinguish benchmark or policy failures from behavior that creates meaningful real-world risk, considering feasibility, attacker uplift, reliability, and the capabilities already available elsewhere. - Build and improve automated testing infrastructure that supports repeatable measurement, rapid iteration, and statistically grounded analysis across models and product surfaces. - Test novel abuse risks in agentic systems, including indirect prompt injection, agent hijacking, and other ways adversaries may manipulate systems that use tools or act on external information. - Translate findings into clear risk assessments and actionable recommendations for Security, Research, Product, Policy, and Engineering partners. - Contribute a portion of your time to Safety Bug Bounty work, particularly where reports require cyber expertise. You might thrive in this role if you: Bring substantial depth in at least one of the following areas: - Cybersecurity, such as application security, penetration testing, vulnerability research, adversary simulation, or red-team operations. You can assess whether a demonstrated capability is reliable, feasible, and meaningfully dangerous. - AI model evaluation, such as designing and running evals, building agentic harnesses, automating adversarial testing, constructing datasets, or analyzing model behavior at scale. You can turn an ambiguous risk question into a reproducible testing approach. Across either profile, you should also have: - Working literacy across both cybersecurity and model evaluation, with an interest in developing further depth outside your primary area. - The ability to write code and build practical testing tools, particularly for automating experiments, orchestrating models, or analyzing results. - An attacker mindset and an interest in discovering failure modes that standard evaluations may not capture. - Clear written and verbal communication, including the ability to explain technical findings, limitations, and risk to audiences with different backgrounds. - Experience working across technical and non-technical teams to move from a finding to a decision, mitigation, or follow-up test. 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 inform

Bring substantial depth in at least one of the following areas: - Cybersecurity, such as application security, penetration testing, vulnerability research, adversary simulation, or red-team operations. - AI model evaluation, such as designing and running evals, building agentic harnesses, automating adversarial testing, constructing datasets, or analyzing model behavior at scale. Across either profile, you should also have: - Working literacy across both cybersecurity and model evaluation, with an interest in developing further depth outside your primary area. - The ability to write code and build practical testing tools, particularly for automating experiments, orchestrating models, or analyzing results. - An attacker mindset and an interest in discovering failure modes that standard evaluations may not capture. - Clear written and verbal communication, including the ability to explain technical findings, limitations, and risk to audiences with different backgrounds. - Experience working across technical and non-technical teams to move from a finding to a decision, mitigation, or follow-up test.

Design and run rigorous evaluations of model cyber capabilities and safeguards, including policy adherence, correct refusal, over refusal, and resilience to jailbreaking and other adversarial techniques. Conduct hands-on testing to understand what models can enable when used by experienced security practitioners, including through task-specific harnesses, scaffolding, and multi-step workflows. Distinguish benchmark or policy failures from behavior that creates meaningful real-world risk, considering feasibility, attacker uplift, reliability, and the capabilities already available elsewhere. Build and improve automated testing infrastructure that supports repeatable measurement, rapid iteration, and statistically grounded analysis across models and product surfaces. Test novel abuse risks in agentic systems, including indirect prompt injection, agent hijacking, and other ways adversaries may manipulate systems that use tools or act on external information. Translate findings into clear risk assessments and actionable recommendations for Security, Research, Product, Policy, and Engineering partners. Contribute a portion of your time to Safety Bug Bounty work, particularly where reports require cyber expertise.