Frontier AI Safety Evaluator & Model Alignment

Mercor; Dublin; Remote; EUR 70,000 - 110,000; Full time

Job summary

Mercor is seeking experienced AI Safety Practitioners to evaluate frontier AI models for safety, alignment, and quality across grey-area topics. You will assess AI-generated responses, apply safety policies, and help improve model behavior through structured evaluations and feedback.

Responsibilities include evaluating outputs for safety and accuracy, reviewing sensitive content, refining RLHF/SFT rubrics, and collaborating with researchers and safety teams to advance AI safety benchmarking.

Qualifications

• Excellent written English, critical thinking, and analytical reasoning skills.
• Ability to consistently evaluate nuanced and policy-sensitive scenarios.
• 5+ years of professional experience in AI Safety, Trust & Safety, journalism, public policy, scientific research, security, or related field.

Responsibilities

• Evaluate AI-generated responses for safety, factual accuracy, policy compliance, and overall quality.
• Review content involving misinformation, political persuasion, self-harm, violence, cyber, biosecurity, and other sensitive domains.
• Apply and refine evaluation rubrics for RLHF, SFT, and AI safety benchmarking.
• Identify unsafe outputs, hallucinations, reasoning failures, and policy violations.
• Provide structured feedback to improve model alignment and safety performance.
• Collaborate with AI researchers and safety teams on ongoing evaluation initiatives.

Skills

Analytical reasoning

Critical thinking

Written English

Policy evaluation

Education

Bachelor's degree or higher

Job description

Mercor is seeking experienced AI Safety Practitioners to evaluate frontier AI models for safety, alignment, and quality across grey-area topics. You will assess AI-generated responses, apply safety policies, and help improve model behavior through structured evaluations and feedback.

Responsibilities include evaluating outputs for safety and accuracy, reviewing sensitive content, refining RLHF/SFT rubrics, and collaborating with researchers and safety teams to advance AI safety benchmarking.