AI Red Teamer
Also known as: red teamer, AI safety red teamer, model red teamer
Red teaming in AI borrows the concept from cybersecurity: a dedicated team tries to break the system in controlled conditions so the cracks get found internally rather than in production. For AI systems, that means crafting prompts designed to bypass safety measures, extract sensitive information, generate harmful content, or expose unintended behaviors.
The role has grown significantly as AI deployments have become more consequential. Anthropic, OpenAI, and Google all run internal red teams, and an ecosystem of third-party firms specializing in AI red teaming has emerged. Hiring in this space is active, with backgrounds ranging from security engineering to social science to creative writing.
Builders deploying AI products need some form of red teaming, even if informal. Building a diverse set of adversarial test cases, especially for prompt injection and jailbreak risks, is increasingly considered table stakes before a production launch. The EU AI Act has formalized adversarial testing requirements for high-risk AI systems.