Data Annotation Contributor (Fully Remote)

Mindy Support; United States; Remoto; USD 220 - 670; Tempo pieno

Descrizione del lavoro

Mindy Support is a remote-first provider offering freelance, task-based data collection work to support enterprise AI training. The program seeks individuals who can access and submit everyday consumer receipts from diverse merchants, using devices with cameras or scanners and reliable internet.

Roles are fully remote with flexible schedules, designed for continuous contribution. Compensation is paid per accepted asset, with onboarding and pilot tasks completed before live submissions.

Mansioni

• Collect and submit diverse transaction receipts to build high-quality datasets.
• Contribute to training enterprise AI models with real-world consumer data.
• Upload legible documents and follow formatting, merchant, and metadata rules.
• Complete onboarding and pilot tasks before live submissions.
• Submit through a secure web platform while maintaining strict data security.

Descrizione del lavoro

• Collect and submit diverse transaction receipts (email, photo, PDF) to build high-quality datasets.
• Contribute to training enterprise AI models with real-world consumer data.

Key Responsibilities:

• Upload legible documents and follow formatting, merchant, and metadata rules.
• Complete brief onboarding and pilot tasks before live submissions.
• Submit through a secure web platform while maintaining strict data security.

Requirements:

• Located in one of the 41 designated project locales.
• Regular access to everyday consumer receipts across merchants.
• Smartphone or computer with camera/scan capabilities and stable internet.
• Freelance crowdsourced role with fully flexible, task-based work.
• Compensation paid per accepted or validated assets.
• Remote work with no fixed schedule.

Mindy Support

Mindy Support provides data annotation and crowdsourced data collection services for training enterprise AI models. They offer freelance, task-based opportunities with flexible schedules and a remote-first approach.