Remote Data Labeling Specialist for AI Training QA

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

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.