Vehicle Damage Expert, Part-Time AI Damage Evaluation – Remote (United States)

You will review real-world automotive evidence and use professional judgment to assess vehicle damage, repairs, condition, and collision details. Your work will help evaluate and improve AI systems through clear, well-supported conclusions.

• Review damage photos, repair estimates, condition reports, and collision scene footage.
• Write detailed vehicle damage estimates and condition assessments using professional appraisal and reconstruction standards.
• Conduct remote accident quality audits and causation analysis.
• Document the evidence and reasoning behind each conclusion.
• Evaluate vehicle condition using criteria from fleet, auction, and original equipment manufacturer inspection programs.
• Provide organized written explanations and verbal feedback.
• Apply current industry practices and maintain consistent work quality.

This is a remote, part-time contractor role.

• $20 to $60 per hour.
• Less than 20 hours per week.
• Remote work, open to candidates in the countries specified for this role, including the United States, Canada, the United Kingdom, France, Germany, India, Japan, Singapore, South Africa, and Vietnam.
• Professional fluency in English.
• At least five years of experience in vehicle damage appraisal, automotive claims, collision repair assessment, or fleet or auction inspection.
• Expertise in auto damage estimating, vehicle inspection, and accident quality auditing.
• Strong analytical and critical-thinking skills, with the ability to review evidence objectively and explain conclusions.
• Excellent written and verbal communication for detailed documentation and reasoned justification.
• Ability to work independently in a remote contributor setting.
• Helpful qualifications include auto damage appraisal, ACTAR reconstruction, OEM auditing, or fleet or auction inspection experience.
• State adjuster licensing, I-CAR, ACTAR, ASE, or state inspection certification can support your qualifications.

AI training is the human work behind systems that learn from examples, including reviewing documents, rating model outputs, and writing clear explanations. People with professional expertise are needed to judge specialized evidence and help make AI results more accurate and useful.