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Open nowPosted 18 days ago

CBRN Specialist

xperteez231 open roles

Where
International
Work mode
Remote
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Your applicationOpen nowCBRN Specialistxperteez · International
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Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 18 days ago

The posting

Type: Contract

Location: Remote

Skills & technologies

  • CBRN
  • English
  • Chemical Safety
  • Biological Safety
  • Radiological Safety
  • Nuclear Safety
  • AI Safety
  • Risk Review
  • Hazard Communication
  • Emergency Preparedness

Key responsibilities

  • Review CBRN-related AI content: Evaluate AI-generated responses involving chemical, biological, radiological, nuclear, hazardous-material, emergency-preparedness, and public-safety topics for accuracy, safety, and guideline alignment.
  • Identify unsafe or enabling content: Flag outputs that provide dangerous procedural detail, acquisition guidance, weaponization support, evasion tactics, unsafe lab guidance, harmful exposure advice, or other misuse-enabling content.
  • Develop safe reference content: Create gold-standard examples that answer benign CBRN questions responsibly while avoiding operationally dangerous instructions.
  • Optimize AI safety behavior: Evaluate and rank model outputs based on safe handling, correct risk framing, appropriate refusals, helpful redirection, scientific accuracy, and clarity.
  • Ensure model integrity: Detect hallucinated claims, incorrect hazard classifications, misleading risk statements, overconfident conclusions, poor escalation guidance, or unsafe recommendations.
  • Support policy-aligned review: Apply project rubrics consistently and provide clear written feedback explaining why content is safe, unsafe, incomplete, or technically incorrect.
  • Maintain technical quality: Ensure CBRN-related content uses precise terminology, avoids exaggeration or minimization of risks, and communicates safety information in a responsible, user-appropriate way.
  • Escalate sensitive cases: Flag ambiguous, high-risk, or potentially harmful content for additional review according to project guidelines.

Requirements:

  • Bachelor’s degree or higher in Chemistry, Biology, Microbiology, Public Health, Toxicology, Nuclear Engineering, Radiation Science, Emergency Management, Environmental Health & Safety, Biosecurity, Security Studies, or a related field, or equivalent professional experience.
  • Strong professional proficiency in English, minimum C1, to follow guidelines and provide detailed written feedback in English.
  • 3+ years of professional experience in CBRN safety, emergency preparedness, hazardous materials, biosafety, radiation safety, public health, environmental health and safety, defense/security analysis, technical review, or related work.
  • Strong understanding of chemical, biological, radiological, and nuclear hazard categories, including safe high-level risk assessment, exposure pathways, protective concepts, emergency communication, and public-safety principles.
  • Ability to evaluate CBRN-related AI outputs for accuracy, safety, completeness, policy alignment, and potential misuse risk.
  • Comfortable identifying unsafe instructions, operationally enabling details, inappropriate specificity, misleading hazard claims, inaccurate emergency advice, or unsupported technical conclusions.
  • Familiarity with safety frameworks, emergency-response concepts, biosafety levels, hazardous-material communication, radiation protection principles, decontamination concepts, incident reporting, and public-health guidance is preferred.
  • High attention to detail when reviewing terminology, units, exposure concepts, risk levels, safety disclaimers, escalation guidance, and sensitive technical boundaries.
  • Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
  • Prior experience with AI safety, data annotation, technical QA, policy review, risk assessment, red-team review, scientific review, or rubric-based evaluation is strongly preferred.
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