Role Description

This role involves building a pod of AI Red-Teamers who probe AI models with adversarial inputs, surface vulnerabilities, and generate the red-team data that makes AI safer for customers.

• Red-team AI models and agents: jailbreaks, prompt injections, misuse cases, exploits
• Generate high-quality human data: annotate failures, classify vulnerabilities, and flag systemic risks
• Apply structure: follow taxonomies, benchmarks, and playbooks to keep testing consistent
• Document reproducibly: produce reports, datasets, and attack cases customers can act on
• Flex across projects: support different customers, from LLM jailbreaks to socio-technical abuse testing

Qualifications

• Prior red-teaming experience (AI adversarial work, cybersecurity, socio-technical probing), highly recommended; OR
• Extensive AI background/education that equips you to learn red-teaming fast
• Curious and adversarial: instinctively push systems to breaking points
• Structured: use frameworks or benchmarks, not just random hacks
• Communicative: explain risks clearly to technical and non-technical stakeholders
• Adaptable: thrive on moving across projects and customers

Nice-to-Have Specialties

• Adversarial ML: jailbreak datasets, prompt injection, RLHF/DPO attacks, model extraction
• Cybersecurity: penetration testing, exploit development, reverse engineering
• Socio-technical risk: harassment/disinfo probing, abuse analysis
• Creative probing: psychology, acting, writing for unconventional adversarial thinking

What Success Looks Like

• Uncover vulnerabilities automated tests miss
• Deliver reproducible artifacts that strengthen customer AI systems
• Evaluation coverage expands: more scenarios tested, fewer surprises in production
• Mercor customers trust the safety of their AI because you’ve already probed it like an adversary

Why Join Mercor

• Build experience in human data-driven AI red-teaming at the frontier of safety
• Play a direct role in making AI systems more robust, safe, and trustworthy