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Job Description

Fully remote and project-based, this role supports flexible scheduling while you help train AI systems for cybersecurity. You will evaluate AI-generated security content, build security-focused problem solutions, and provide feedback that directly improves future AI security models. Projects are paid hourly at $50 to $100 USD, with the possibility of higher bonus rates for select work.

Payment is processed via PayPal. DataAnnotation will never ask for any money from you. This opportunity is available to candidates located in the US, Canada, UK, Ireland, Australia, and New Zealand.

What you’ll do

  • Evaluate AI-generated cybersecurity content, including threat analysis, vulnerability assessments, and offensive security techniques, with an emphasis on real-world accuracy and validity.
  • Design and solve security-focused technical problems that are used to train AI systems.
  • Write clear technical explanations and security-relevant code.
  • Provide feedback that informs and shapes the next generation of AI models for cybersecurity.

What you bring

  • 2+ years of hands-on experience in a cybersecurity role, such as penetration testing, red teaming, incident response, detection engineering, DFIR, malware analysis, threat intelligence, or adjacent fields, including government or military cyber operations.
  • Coding experience is required, with comfort navigating and patching a codebase.
  • English fluency (native or bilingual level) and strong writing skills.
  • Bachelor’s degree preferred.
  • Certifications are a plus, such as OSCP, CRTO, GREM, GCFA, CEH, and similar.

Location, schedule, and pay

  • Location: Seattle, WA (remote)
  • Work model: Fully remote
  • Schedule: Flexible, with the ability to choose which projects you take on and when you work
  • Equipment: Work is performed on your own computer
  • Compensation: $50 to $100 USD per hour, with potential higher bonus rates for certain projects

Impact: Help shape AI technologies built for cybersecurity by validating content, solving security-focused technical challenges, and providing feedback that improves model performance.

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