Chemical Safety Expert for AI Model Safety Audit

Type: contract

Location: remote

Interview: Required

Interview language: English

About this role

Description

We are hiring chemists and chemical safety 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 (benign, dual-use or adversarial), mark each answer as safe or unsafe and say why, 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 be able to get an answer.

The requests are realistic: what synthetic and analytical chemists, toxicologists, industrial hygienists, process safety engineers, regulators and occasionally bad actors actually ask. Some are ordinary professional questions that models wrongly refuse, and catching those matters as much as catching the dangerous ones.

Who we're looking for

Synthetic and analytical chemists; toxicologists and pharmacologists; industrial hygienists and chemical safety/process safety professionals; CWC or chemical-security specialists; hazmat, poison-control or chemical incident responders; and chemical regulators (EPA, OSHA, HSE, OPCW-adjacent, or equivalent). Usually a relevant degree plus five or more years of professional experience. Experience with safety data sheets, exposure assessment, incident reports or regulatory review is a strong plus.

Before you apply

Open only to citizens or permanent residents of the US, UK, Canada, Australia, New Zealand and the European Union.

Your profile

• Bachelor’s degree or higher in Chemistry, Chemical Engineering, Toxicology, Environmental Health & Safety, Industrial Hygiene, Hazardous Materials Management, or a closely related field; advanced degree or professional safety certification is a strong plus.

• 3+ years of professional experience in chemical safety, laboratory safety, hazardous materials handling, EHS, toxicology, industrial hygiene, chemical risk assessment, regulatory compliance, or related technical safety work.

• Strong understanding of chemical hazards, including toxicity, corrosivity, flammability, reactivity, incompatibilities, exposure routes, storage risks, spill response principles, PPE, ventilation, and safe handling practices.

• Familiarity with chemical safety documentation and frameworks such as Safety Data Sheets, GHS hazard communication, labeling, exposure limits, risk assessments, SOPs, and incident escalation procedures.

• Ability to review AI-generated chemical safety content for technical accuracy, safety alignment, misuse risk, hallucinated claims, inappropriate specificity, and potentially dangerous procedural guidance.

• Strong judgment in distinguishing between safe educational content and content that could enable unsafe chemical handling, synthesis, misuse, evasion, or harmful experimentation.

• Excellent written English skills, with the ability to provide clear, structured feedback explaining why a response is safe, unsafe, incomplete, misleading, or technically incorrect.

• High attention to detail when reviewing chemical terminology, units, concentrations, exposure assumptions, hazard classifications, safety controls, and emergency-response guidance.

• Comfortable applying detailed project rubrics consistently across ambiguous or sensitive chemical safety scenarios.

• Prior experience with AI safety, red-team review, model evaluation, content moderation, data annotation, technical QA, scientific review, or rubric-based evaluation is preferred.

Key responsibilities

• Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.

• Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.

• Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.