AI Trainer - Automotive Expertise Required

Accountabilities:

• Review and analyze vehicle damage photos to identify scratches, dents, cracks, missing parts, and other visible issues.


• Accurately classify damaged vehicle components such as doors, bumpers, fenders, hoods, and related parts.


• Follow detailed annotation guidelines and quality standards to ensure data accuracy and consistency.


• Support the creation and improvement of high-quality datasets used for AI model training and evaluation.


• Contribute to efficient insurance claim assessment processes through precise image review and labeling.


• Maintain productivity and quality while working independently in a remote freelance environment.


• Participate in project updates or process improvements when required.

Requirements

• Professional experience in automotive-related fields such as insurance claims, vehicle appraisal, auto repair, body shop work, mechanics, dealerships, or automotive parts.


• Strong ability to identify vehicle damage types and recognize automotive components accurately.


• High attention to detail and ability to follow structured written and visual guidelines.


• Comfortable working independently and managing tasks in a remote environment.


• Basic computer literacy and ability to work with digital annotation or review tools.


• Strong reliability, consistency, and quality-oriented mindset.


• Must be based within the European Union.


• Previous experience with image annotation, AI training projects, or quality review processes is considered a plus.

• 100% remote freelance opportunity with flexible working hours.


• Ability to work independently and at your own pace.


• Competitive freelance compensation of approximately €11/hour.


• Opportunity to contribute to innovative AI and machine learning initiatives.


• Potential for increased workload and long-term project extension as operations scale.


• Ideal side-income opportunity with flexible scheduling.


• Clear onboarding materials and visual training resources provided.

Questo annuncio proviene da ats_lever. Vedi l'annuncio originale ↗