Merchandise Planner AI Evaluator

As a Merchandise Planner AI Evaluator, you will help improve AI systems by creating expert training data and reviewing AI-generated responses. Your feedback will show whether responses accurately reflect real retail planning work.

You will work independently and asynchronously on projects involving retail merchandising, inventory decisions, forecasting, and promotional planning.

• Create training data based on retail merchandising and merchandise planning practice.
• Evaluate AI-generated responses for accuracy and relevance.
• Apply professional judgment to demand forecasting and in-season reforecasting.
• Review markdown optimization, hindsight analysis, and promotional campaign planning.
• Explain retail planning decisions clearly and provide accurate, detailed feedback.

What it pays and takes

This is a remote, part-time contract role for candidates in the United States. Prior AI experience is not required, but you should be able to apply practical retail planning knowledge in written evaluations.

• Pay: Up to $80 per hour.
• Time: 20+ hours per week.
• Schedule: Flexible and asynchronous.
• Location: Remote; open to candidates in the United States.
• Language: Fluent in English.
• Experience: At least two years of professional experience in retail merchandising or merchandise planning.
• Relevant roles: Merchandise Planner, Senior Merchandise Planner, or an equivalent retail position.
• Required knowledge: Demand forecasting, in-season reforecasting, markdown optimization, hindsight analysis, and promotional campaign planning.
• Skills: Strong written communication, attention to detail, and the ability to work independently with AI research teams.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to use and helps you find AI training work in one place.

About AI training work

AI training work is the human side of building artificial intelligence. People create examples and review model responses so AI systems can produce more accurate and useful results, while experienced professionals help models handle real work in specialized fields.