Remote Data Labeling Specialist (Vancouver)

Remote Data Labeling Specialist (Vancouver) — Full-Time Rex.zone is hiring a Remote Data Labeling Specialist to deliver consistent, policy-aligned annotations used to train and evaluate AI/ML models. You will work across NLP labeling, RLHF preference ranking, prompt evaluation, computer vision annotation, and content safety labeling to improve training data quality and downstream model performance. Key Responsibilities Apply annotation guidelines accurately across NLP and computer vision tasks Perform RLHF preference ranking and rubric-based evaluations to align model behavior with human preferences Execute prompt evaluation and response quality scoring for LLM evaluation workflows Conduct QA evaluation, sampling audits, guideline compliance checks, and error categorization to reduce label noise Label content safety categories (e.g., harassment, self-harm, hate) using policy taxonomies Collaborate with project leads to resolve edge cases, refine rubrics, and document decisions Track productivity and accuracy metrics to support model performance improvement Workstreams You May Support NLP data labeling: named entity recognition (NER), intent classification, sentiment/topic labeling, summarization evaluation LLM training pipelines: instruction-following evaluation, pairwise preference ranking, rubric grading Computer vision annotation: bounding boxes, polygons, keypoints, segmentation, OCR validation Content safety labeling: policy-based moderation labels and severity scoring Multimodal tasks: text-image relevance, caption validation, VQA-style evaluation Required Qualifications Experience applying structured guidelines to data annotation and/or QA evaluation work Strong written communication for consistent labeling rationale and disagreement resolution Comfort working with ambiguity, edge cases, and evolving rubrics Familiarity with NLP, RLHF, or LLM evaluation concepts (training data quality, preference ranking, prompt evaluation) Ability to maintain accuracy at production scale and meet full-time delivery expectations Preferred Qualifications Experience with annotation platforms and audit workflows Exposure to computer vision annotation tools (boxes, polygons, segmentation) Experience with content safety labeling or policy taxonomy interpretation Experience documenting guidelines, building QA checklists, or running inter-annotator agreement checks Remote Work Notes This is a Remote role. You can be based in Vancouver while working with distributed teams. Projects may require overlap with US business hours, and task availability can vary by domain. Pay: $30–$50 per hour (base salary).

The specialist will provide high-quality annotations and preference rankings across NLP and computer vision tasks to train AI/ML models. Responsibilities include conducting QA audits, evaluating LLM responses, and labeling content safety categories based on policy taxonomies.

Candidates must have experience applying structured guidelines to data annotation or QA evaluation and be familiar with RLHF or LLM evaluation concepts. Strong written communication and the ability to handle ambiguity and evolving rubrics are required.

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Data Labeling
• NLP
• RLHF
• Computer Vision Annotation
• Content Safety Labeling
• Prompt Evaluation
• QA Evaluation
• Named Entity Recognition
• Sentiment Analysis
• Instruction-Following Evaluation
• Bounding Boxes
• Segmentation
• OCR Validation
• Policy Taxonomy Interpretation
• Inter-annotator Agreement
• Written Communication

• Data & Analytics
• Technology
• Software
• Science & Research
• Engineering

Expérience minimale

2+ ans

Postuler avant le

29 août 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Exigences de lieu

Country, Vancouver, British Columbia, Canada

Niveau d’expérience

Mid-Senior level