Customer Service Expert (Phone Support)

You will make and receive calls with businesses and service providers across the United States. Your conversations will provide real-world examples used to train AI systems, while you complete practical customer requests clearly and professionally.

• Place and receive calls for reservations, appointments, vendor confirmations, and other customer requests.
• Gather information and fulfill requests with businesses and service providers.
• Navigate phone menus, hold queues, and call-center systems to reach the right contact.
• Record call outcomes and important details accurately.
• Handle sensitive information discreetly.
• Manage many short, consecutive calls across different customer needs.
• Work with other remote professionals to maintain consistent service quality.

What it pays and takes

This is a remote, part-time contractor engagement with an initial four-week term. Four five-hour shifts are scheduled each week, with some weekend availability required.

• Pay: $18 to $28 per hour for scheduled, available hours, not per call completed.
• Location: Open to candidates in Argentina, Brazil, Chile, Colombia, Mexico, and Peru.
• Schedule: Four five-hour shifts per week, up to 20 hours weekly.
• Language: Clear, neutral, professional spoken English for conversations in the United States.
• You must be confident with outbound calls, automated phone systems, and extended hold queues.
• You need strong live conversation, improvisation, judgment, and professional communication skills.
• You must work independently and manage consecutive tasks accurately.
• You need reliable high-speed internet, a quiet work environment, and a quality wired or USB headset.
• Helpful experience includes executive assistance, virtual assistance, concierge services, phone-based customer support, hospitality, restaurants, travel, negotiation, or escalation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training and data-labeling work uses human examples and reviews to help artificial intelligence systems improve. Phone conversations, recordings, and accurate task outcomes can help models learn how people communicate and how real customer requests are handled.