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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
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