mercor.com
Contractor
remote: US & Europe
$50.5/hr
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AI Red-Teamer — Adversarial Testing | $50.5/hr | Remote (US & Europe) | English & French Required
Mercor is assembling an elite red team to probe AI models with adversarial inputs, surface vulnerabilities, and generate critical safety data. Help us make AI safer by attacking it first—before adversaries do.
Type: Full-time or Part-time Contract Work Language Requirement: Native-level fluency in both English and French is required.
Why This Role Exists
We believe the safest AI is one that's already been tested under adversarial conditions. As an AI Red-Teamer, you'll be a human data expert who probes conversational AI models, identifies weaknesses, and creates the datasets that strengthen AI safety for our customers.
Note: This project involves reviewing AI outputs on sensitive topics including bias, misinformation, and harmful behaviors. All work is text-based, and participation in higher-sensitivity projects is optional with clear guidelines and wellness resources provided.
What You'll Do
Red team conversational AI models and agents: execute jailbreaks, prompt injections, misuse cases, bias exploitation, and multi-turn manipulation attacks
Generate high-quality human data: annotate failures, classify vulnerabilities, and flag systemic risks
Apply structured methodologies: follow taxonomies, benchmarks, and playbooks to ensure consistent testing
Document reproducibly: produce actionable reports, datasets, and attack cases for customers
Who You Are
Experienced in red teaming (AI adversarial work, cybersecurity, or socio-technical probing)
Naturally curious and adversarial: you instinctively push systems to their breaking points
Methodical and structured: you use frameworks and benchmarks, not just random exploits
Excellent communicator: you explain risks clearly to both technical and non-technical stakeholders
Highly adaptable: you thrive when moving across diverse projects and customers
Nice-to-Have Specialties
Adversarial ML: jailbreak datasets, prompt injection, RLHF/DPO attacks, model extraction
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