Remote Data Labeling Jobs in Canada (Full Time)Rex.Zone supports AI/ML training pipelines through data labeling, RLHF evaluation, prompt evaluation, and QA checks. You will apply annotation guidelines compliance to improve training data quality for large language models and computer vision systems.About The RoleYou will label and evaluate text, image, and multimodal data used to train and validate machine learning models. Typical work includes LLM response grading, RLHF preference labeling, prompt evaluation, entity tagging for NLP, bounding boxes and segmentation for computer vision annotation, and content safety labeling. You will follow annotation guidelines, document edge cases, and complete QA evaluation checks to ensure dataset consistency and high training data quality.Key ResponsibilitiesProduce accurate labels for NLP and computer vision tasksPerform RLHF ranking and pairwise preference judgments for LLM trainingExecute prompt evaluation and rubric-based scoring for model outputsApply named entity recognition and taxonomy taggingComplete content safety labeling with clear rationalesRun QA evaluation workflows including audits, cross-checks,



and error analysisTrack annotation guidelines compliance and propose guideline improvementsEscalate ambiguous cases and contribute to calibration sessions that improve inter-annotator agreementRequired QualificationsProfessional experience in data labeling, data annotation, QA evaluation, or trust and safetyStrong attention to detail and ability to follow annotation guidelinesComfortable working with web-based annotation tools and spreadsheetsAbility to explain decisions clearly using rubrics, rationales, and examplesFamiliarity with NLP concepts such as named entity recognition and text classificationAvailability for full-time work from home with reliable connectivityPreferred QualificationsExperience with RLHF workflows, LLM evaluation, and prompt evaluationExposure to computer vision annotation (bounding boxes, polygons, segmentation masks, keypoints)Understanding of training data quality metrics (accuracy, consistency, coverage, bias)Prior work with content safety labeling and policy interpretationExperience collaborating with AI labs, tech startups, BPOs, or annotation vendorsTools and WorkflowsYou may use labeling platforms, internal QA dashboards, and guideline repositories. Workflows can include gold-standard calibration, blind reviews, inter-annotator agreement checks, and structured error analysis aimed at model performance improvement for production AI systems.#J-18808-Ljbffr

📌 Remote Data Labeling Specialist (Canada) - Remote (Boucherville)
🏢 Rex.zone
📍 Boucherville