Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading)

confidential; United States; Remote; USD 34,000 - 55,000; Part time

Job summary

Zekai seeks a Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) to support remote training data for AI models. You will apply certified visual grading expertise to assess softwood lumber, identify defects, and justify grade decisions using established rulebooks.

The role emphasizes detailed documentation, adherence to NLGA/WWPA/SPIB/WCLIB standards, and clear communication of outcomes to support model learning and quality control in a remote environment.

Qualifications

• Active or recent certification as a softwood lumber grader.
• Experience in visual lumber grading and defect identification across softwood species.
• Ability to document decisions with rulebook references and rationale.

Responsibilities

• Review lumber descriptions and images for visual grading per NLGA/WWPA/SPIB/WCLIB rules.
• Identify and measure defects (knots, wane, checks, shake, warp) and set grade limits.
• Make grade calls based on rulebook criteria and document reasoning.
• Evaluate grade stamping accuracy and provide feedback on compliance.
• Analyze grade recovery and quality control scenarios for optimization.
• Reference the specific rule(s) justifying each grade decision.
• Deliver written and verbal explanations to support AI model training.

Skills

Lumber grading

Defect identification

Technical documentation

Education

Certified softwood lumber grader

Tools

NLGA/WWPA/SPIB/WCLIB standards

Job description

Role Title: Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading)

Role Type: Contractor

Location: Remote

our partner 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.

This is a 100% remote contract role, open to applicants worldwide in the countries the employer accepts.