Supply Chain AI Trainer (Remote Contract)

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About AI Training Work

AI training (data labeling / human feedback) is the human side of modern AI — people prepare, review, and rate examples that teach models how to behave. This role centers on reviewing and improving text-based outputs from AI systems in supply chain contexts, directly influencing how models reason about logistics, procurement, and vendor relationships.

• Common tasks include evaluating model responses, rating alternatives, and providing structured feedback for model improvement.
• Work is remote and flexible; many contributors balance this work part-time with other commitments.

The Role

Micro1 seeks a Supply Chain AI Trainer with deep wholesale, logistics, and global procurement experience to provide high-quality feedback on AI outputs. You will assess AI-generated documentation, simulate supply chain scenarios, and evaluate freight schedules and vendor SLAs to improve model reasoning in real-world supply chain tasks.

This is a contract, part-time role (20+ hours/week). Compensation is hourly (USD $40–$65/hr) and work is performed remotely. Tasks involve text data and label types focused on evaluation/rating and text generation, using a proprietary or other labeling platform.

• Employment type: Contractor, Part-time.
• Time commitment: 20+ hours per week.
• Data type: Text; Labeling focuses on evaluation rating and text generation.

What You'll Do

Provide subject-matter feedback and structured critiques that help models make accurate, practical supply chain decisions. Your domain knowledge will guide improvements to AI outputs and prompt design for wholesale and global procurement scenarios.

• Simulate and assess wholesale supply chain operations with attention to efficiency and scalability.
• Evaluate and optimize freight distribution schedules and vendor SLAs in global procurement contexts.
• Provide detailed input on purchasing workflows, vendor selection, and contract negotiation.
• Review AI-generated documentation for accuracy, completeness, and industry relevance.
• Offer actionable feedback to strengthen models' logical reasoning in supply chain scenarios.
• Analyze logistics workflows and recommend improvements aligned with wholesale best practices.
• Document findings clearly in written and verbal form to support knowledge sharing.

Requirements

Candidates must bring substantial, hands-on supply chain experience and clear communication skills. Every requirement below is essential for success in this role.

• 5–10+ years of end-to-end supply chain management experience.
• Deep focus on wholesale, logistics, and global procurement.
• Proven success in purchasing, vendor negotiations, and contract management.
• Excellent English communication skills, both written and verbal.
• Ability to provide detailed, structured feedback and clear documentation.
• Strong analytical and critical thinking skills with a solution-oriented mindset.
• Availability of 20+ hours per week.

Preferred Qualifications

These experiences are not required but will help you ramp faster and contribute more effectively to the project.

• Experience collaborating in remote, distributed teams across multiple regions.
• Prior exposure to AI, machine learning, or technology-driven supply chain solutions.
• Strong familiarity with wholesale operations from a global perspective.

How It Works & Next Steps

If you meet the requirements, you will be contracted by Micro1 and onboarded to the project's labeling platform. Expect to review examples, rate model outputs, and submit structured feedback in regular batches.

OpenTrain makes it easy to apply and build a reputation across similar projects; create a profile if you don't already have one and submit your application for this listing.

• Apply with your OpenTrain profile and resume highlighting supply chain experience.
• Onboarding includes project guidelines, sample tasks, and a short quality check before active work.
• Work will be paid hourly at the stated rate and scheduled according to your availability within the 20+ hr/week expectation.