Softwood Lumber Grader and Sawmill Quality Supervisor

You will help train AI systems by reviewing sawn softwood lumber descriptions, images, and quality control cases. Your decisions will be based on recognized lumber-grading rules and clear explanations of the evidence behind each grade call.

• Identify defects such as knots, wane, checks, shake, and warp.
• Make visual grade decisions using NLGA, WWPA, SPIB, or WCLIB rules.
• Explain the rule basis for each grading decision in writing and verbally.
• Review grade stamp accuracy and analyze grade recovery and quality control cases.
• Maintain consistent, technically accurate decisions across repeated review tasks.

This is remote contractor work. Weekly scheduling and assignment duration depend on project needs, so the listing does not set a fixed weekly schedule.

• Pay: $20 to $60 per hour.
• Location: Remote, with eligibility in the countries included in this listing.
• Language: Fluent English.
• Knowledge: Practical visual grading experience with common softwood species and strong knowledge of industry grading rules.
• Helpful background: Current or recent certification as a softwood lumber grader or sawmill quality control supervisor from a recognized agency.
• Additional skills: Familiarity with grade stamping, grade recovery metrics, and sawmill quality control practices.
• Strong attention to detail, technical documentation skills, and clear communication are required.
• Recent practical experience in a sawmill environment is valued.

You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.

The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 7 screening questions.

AI training is the human work behind systems that learn from examples. People with real subject knowledge review information, make careful judgments, and explain their decisions so AI systems can produce more reliable results.