Remote Data Labeling Specialist (Canada)

Rex.zone; Canada; Remote; CAD 48,000 - 68,000; Full time

Job summary

Rex.zone is hiring for remote data labeling roles in Canada. You will label and evaluate text, image, and multimodal data used to train ML models, including LLM response grading, RLHF labeling, and content safety assessment.

Join a team applying annotation guidelines, documenting edge cases, and performing QA checks to ensure high-quality training data. Strong attention to detail and experience with NLP concepts are essential for success.

Qualifications

• Professional experience in data labeling, annotation, QA evaluation, or trust and safety.
• Strong attention to detail and ability to follow annotation guidelines.
• Comfortable using web-based annotation tools and spreadsheets.
• Ability to explain decisions clearly using rubrics, rationales, and examples.
• Familiarity with NLP concepts such as named entity recognition and text classification.
• Availability for full-time remote work with reliable connectivity.

Responsibilities

• Produce accurate labels for NLP and computer vision tasks.
• Perform RLHF ranking and pairwise preference judgments for LLM training.
• Execute prompt evaluation and rubric-based scoring for model outputs.
• Apply named entity recognition and taxonomy tagging.
• Complete content safety labeling with clear rationales.
• Run QA evaluation workflows including audits, cross-checks, and error analysis.
• Track annotation guidelines compliance and propose guideline improvements.
• Escalate ambiguous cases and contribute to calibration sessions that improve inter-annotator agreement.

Skills

Data labeling

Attention to detail

Rubrics & rationales

NLP concepts

Remote work

Tools

Annotation tools

Job description

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 Role

You 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 Responsibilities

• Produce accurate labels for NLP and computer vision tasks
• Perform RLHF ranking and pairwise preference judgments for LLM training
• Execute prompt evaluation and rubric-based scoring for model outputs
• Apply named entity recognition and taxonomy tagging
• Complete content safety labeling with clear rationales
• Run QA evaluation workflows including audits, cross-checks, and error analysis
• Track annotation guidelines compliance and propose guideline improvements
• Escalate ambiguous cases and contribute to calibration sessions that improve inter-annotator agreement

Required Qualifications

• Professional experience in data labeling, data annotation, QA evaluation, or trust and safety
• Strong attention to detail and ability to follow annotation guidelines
• Comfortable working with web-based annotation tools and spreadsheets
• Ability to explain decisions clearly using rubrics, rationales, and examples
• Familiarity with NLP concepts such as named entity recognition and text classification
• Availability for full-time remote work with reliable connectivity

Preferred Qualifications

• Experience with RLHF workflows, LLM evaluation, and prompt evaluation
• Exposure 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 interpretation
• Experience collaborating with AI labs, tech startups, BPOs, or annotation vendors

Tools and Workflows

You 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.