
Inventory Control Manager - Remote Fellowship
Listing checked October 23, 2026 · pay as published by Handshake AI
Key details
- Pay
- $80/hr
- Location
- RemoteUnited States
- Job type
- Part-Time
- Posted
- April 29, 2026
Overview
Handshake AI seeks seasoned retail warehouse and inventory professionals for a part-time fellowship that feeds real-world expertise into AI research. You will build expert-level questions and scenarios from actual retail inventory workflows, then judge how well AI responses match inventory reconciliation, demand forecasting, and in-season reforecast practices. The project runs on your schedule, with no minimum hours, so you can keep your current role and contribute when it fits. Pay runs at $80 per hour, and the work centers on written evaluations rather than day-to-day warehouse operations.
What You'll Do5
- Create expert-level training questions and scenarios that mirror real retail inventory workflows.
- Evaluate AI-generated answers for accuracy and relevance to inventory management and forecasting practices.
- Write structured feedback that helps improve model performance.
- Complete tasks at your own pace without a set schedule or minimum hour requirement.
- Draw on hands-on inventory management, warehouse operations, and materials handling experience to assess AI content.
Requirements4
- At least five years of professional experience in warehouse or inventory management, with titles such as Inventory Control Manager, Inventory Control Analyst, Warehouse Operations Manager, WMS Analyst, Distribution Center Supervisor, Materials Management Analyst, or Automation Engineer.
- Hands-on work with inventory reconciliation, demand forecasting, and in-season reforecast.
- Familiarity with Manhattan Associates, Blue Yonder, Swisslog, HighJump, or comparable warehouse management and inventory systems.
- Strong written communication skills, close attention to detail, and the ability to work on your own in a remote, asynchronous setting.
Who Should Apply
Candidates with five or more years in warehouse or inventory management fit best, and those who have owned inventory reconciliation, demand forecasting, or in-season reforecast work stand out. Experience with Manhattan Associates, Blue Yonder, Swisslog, HighJump, or similar systems matters because the training tasks hinge on real warehouse management workflows. The role suits professionals who write clear evaluations on their own and can commit to part-time project work without a fixed schedule. Applicants who lack hands-on inventory or warehouse experience, or who have never used a warehouse management system, will score low on fit. Low fit often comes from vague written feedback that fails to tie each judgment to a specific inventory practice, or from answers that ignore the retail inventory context the project needs.
Salary Insight
The listing states $80 per hour. For part-time AI training work that calls for five or more years of warehouse and inventory expertise, this rate sits near the upper end of typical project-based evaluation pay. Part-time AI training work with this level of warehouse and inventory specialization often pays less, so the figure reflects the value placed on five or more years of hands-on experience.
Pay and demand for AI Training roles
Market estimates from live NearSkill listings — separate from this job's published terms.
This role · as published
$80/hr
Taken from the listing itself.
Typical pay · estimated
$68/hour
Median of 1,169 similar roles that publish pay · 299 publish only a top rate; those count at the rate they gave
Most AI Training roles pay $38–$95 per hour. This role's pay falls inside that range.
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
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- Listed in last 30 days
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Hiring most right now: micro1 (331) · SME Careers (253) · Mercor (201)
Most requested skills · share of roles
- ai evaluation15%
- llm evaluation13%
- data annotation13%
- ai training13%
Figures from AI Training roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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