Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading)
micro1
At a glance
Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) is a remote other opening at micro1. The posting places it in Remote. The post date on the listing is 8 Nov 2026. No deadline is filled in on the listing. The notes below are written by Akazi. The advertisement under them is the employer's own text.
- Company
- micro1
- Job type
- Other · Remote
- Location
- Remote
- Deadline
- Deadline not listed
- Date posted
- 8 Nov 2026
How to apply
- Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) is listed here so you can find it. micro1 takes the application. Akazi does not.
- Use Apply. It opens the employer's own site. Finish every step there, including an account if they ask for one.
- No deadline is stored in the listing fields. Read the advert in case the date is only written in the text, and apply while it is still open.
- It is remote (Remote). Say that you are based in Rwanda, which hours you can overlap with the team, and which languages you work in.
- Shape your CV around this other role and put the experience they ask for near the top. Name Kinyarwanda, English, or French only at the level you can actually work in.
- Do not pay a fee, buy a starter kit, or share a mobile-money PIN. A real application to micro1 will not ask you to pay in order to be hired.
Writing a CV for Rwanda · Preparing for interviews · Remote work from Rwanda
Apply(opens in a new tab)Employer advertisement
The text below is the public posting from micro1. Akazi did not write it.
Role Title : Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading)
Role Type : Contractor
Location : Remote
micro1 is engaging Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) experts to contribute their highly specialized visual grading skills and sawmill quality control experience to a customer's project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. The assignment focuses on evaluating and providing detailed, rule-based feedback on softwood lumber grades, defects, and quality control cases. If you have current or recent grading agency certification and hands-on knowledge of NLGA, WWPA, SPIB, or WCLIB standards, this is an excellent opportunity to leverage your expertise while working remotely.
Scope of Work
Review and assess descriptions and images of sawn softwood lumber for accurate visual grading according to published rules (NLGA, WWPA, SPIB, WCLIB). Identify and measure lumber defects including knots, wane, checks, shake, and warp, and determine corresponding grade limits. Make clear grade calls on sample pieces based on rulebook criteria, documenting the decision-making process in writing. Evaluate grade stamping accuracy and provide feedback on proper application and compliance. Analyze grade recovery and quality control scenarios, offering insight into optimization possibilities. Reference the specific rule(s) that justify each grade decision, ensuring technical rigor. Deliver written and verbal explanations for decisions to support AI model training and development.
Preferred Qualifications
Active or recent certification as a softwood lumber grader or sawmill quality control supervisor from a recognized agency (e.g., NLGA, WWPA, SPIB, WCLIB). Demonstrated expertise in visual lumber grading and defect identification across common softwood species. Proven ability to interpret and apply industry grading rules with confidence and accuracy. Experience providing written and verbal feedback or training on quality standards. Familiarity with grade stamping procedures, grade recovery metrics, and quality control best practices. Strong attention to detail, technical documentation skills, and ability to articulate decisions clearly. Recent practical experience in a sawmill environment is highly valued.
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