Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) is a remote evaluation track for reviewing softwood lumber grader / sawmill quality supervisor evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn softwood lumber grader / sawmill quality supervisor evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.
Responsibilities
Evaluate softwood lumber grader / sawmill quality supervisor evaluation model outputs against a versioned rubric and assign severity tags for Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) assignments.
Compare paired responses and pick the stronger answer with a written rationale.
Label hallucinations, instruction-following failures, and unsafe content with structured tags.
Capture ambiguous prompts and route them back to the program team for rubric updates.
Maintain reviewer-quality scores by calibrating against gold-standard examples each week.
Document recurring failure modes so the modeling team can target them in the next training run.
What you should bring
Prior evaluation, annotation, or human-rater experience on softwood lumber grader / sawmill quality supervisor evaluation or adjacent content for Softwood Lumber Grader / Sawmill Quality Supervisor (Visual Grading) work.
Comfort applying multi-page rubrics consistently across long batches.
Clear written reasoning that names the issue and the rubric clause being applied.
Strong attention to detail and the ability to flag when a prompt itself is the problem.
Reliable async availability for at least 10 hours per week.
Role signals
Example tasks
Compare two softwood lumber grader / sawmill quality supervisor evaluation model responses to the same prompt and pick the stronger one with rationale.
Tag an unsafe response with the correct policy category and severity.
Audit a 50-row batch for rubric consistency and report drift to the program lead.
Propose a rubric clarification after spotting a recurring failure mode.
Useful experience
Background in linguistics, content moderation, or trust & safety review.
Experience with inter-rater agreement metrics and calibration cycles.
Domain expertise that lets you spot subject-matter errors automated checks miss.
Compensation and schedule
Hourly rate confirmed after the interview process.
Expected arrangement: contractor, with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.
AuraOne is a human data company for AI teams and a workflow service for enterprises. It supplies expert judgment, speech, physical-world data and checked workflows.
