AI Safety Experts — English & Dutch | $48-$62/hr Remote
Overview
Mercor is assembling a remote red team of AI safety experts who speak both English and Dutch fluently. Your job is to probe conversational AI models with adversarial inputs, uncover hidden vulnerabilities, and produce the human data needed to make AI safer. This hourly role puts you at the frontier of AI safety, working on text-based projects that tackle sensitive topics like bias and misinformation.
What You'll Do4
- 1Stress-test conversational AI models and agents using techniques like jailbreaks, prompt injections, misuse cases, and multi-turn manipulation.
- 2Generate high-quality human data by annotating model failures, categorizing vulnerabilities, and flagging systemic risks.
- 3Follow structured taxonomies, benchmarks, and playbooks to keep your testing consistent and repeatable.
- 4Document every attack and finding in reproducible reports, datasets, and case studies that customers can act on.
Requirements5
- 1Hands-on experience red teaming AI systems, whether from adversarial machine learning, cybersecurity, or socio-technical probing.
- 2A naturally adversarial mindset that enjoys pushing systems to their breaking points.
- 3Comfort using frameworks or benchmarks to guide testing, not just relying on random attempts.
- 4Strong ability to explain technical risks clearly to both technical and non-technical stakeholders.
- 5Flexibility to shift across projects and customer needs as the work evolves.
Who Should Apply
You're the type of person who looks at an AI system and immediately thinks about how to break it. You have prior red teaming or security experience, you're methodical in your approach, and you're comfortable handling sensitive content like bias or misinformation. You're also independent enough to work remotely and fluent in both English and Dutch.
Salary Insight
Hourly compensation ranges from $48.00 to $62.00 per hour.
Location
Required Skills
Application Tip
When you apply, include a specific example of a time you uncovered a vulnerability that automated tests missed — detail your approach, the tools you used, and how you documented the finding.
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