Explosives & Energetic Materials AI Safety Auditor

SME Careers; Australia; On-site; AUD 120,000 - 190,000; Full time

Job summary

SME Careers is hiring explosives and energetic materials professionals to audit how AI models respond to sensitive questions in the field. Each task gives you a user request and the answers three different models gave to it.

You classify the request, rank the three against each other, and write the answer the model should have given. For dual-use requests you also note what makes it sensitive, what legitimate purpose it serves, and who should access the results.

Qualifications

• Bachelor’s degree or higher in Chemistry, Chemical Engineering, Materials Science, Energetic Materials, Explosives Engineering, Safety Engineering, Hazardous Materials Management, Environmental Health & Safety, or closely related field.
• 3+ years of professional experience in explosives safety, energetic materials, hazardous materials safety, chemical safety, defense safety, industrial safety, EHS, regulatory compliance, risk assessment, or related work.
• Strong understanding of explosives and energetic materials safety concepts and handling frameworks.
• Experience reviewing AI-generated content for safety, accuracy and risk.

Responsibilities

• Classify user requests as benign, dual-use or adversarial.
• Rank model responses by safety and usefulness.
• Explain why each answer is safe, unsafe, or incomplete.
• Provide guidance on legitimate safety constraints and compliance.

Skills

Written English

Attention to detail

Risk assessment

Technical safety review

Content moderation

Judgment skills

Safety frameworks

Education

Bachelor’s degree or higher in Chemistry or related field

Advanced degree or professional safety certification (strong plus)

Job description

SME Careers is hiring explosives and energetic materials professionals to audit how AI models respond to sensitive questions in the field. Each task gives you a user request and the answers three different models gave to it.

You classify the request, rank the three against each other, and write the answer the model should have given. For dual-use requests you also note what makes it sensitive, what legitimate purpose it serves, and who should access the results.