AI Data Labeling Jobs Canada

AI data labeling jobs Canada at Rex.zone focus on creating and evaluating high-quality training data for modern AI systems. In this remote, full-time role, you will label and review text, image, and multimodal datasets used in large language model evaluation, RLHF workflows, and computer vision annotation. You will follow annotation guidelines compliance, perform QA evaluation, and run prompt evaluation to improve training data quality and model performance improvement. This job supports LLM training pipelines for AI labs, tech startups, and annotation vendors, with consistent feedback loops, throughput targets, and content safety labeling where needed.

Job Overview

Keyword + Job Title: AI Data Labeling Jobs Canada Title: AI Data Labeling Jobs Canada Date: 25-02-2026 Company: Rexzone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: AI data labeling, data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About Rex.zone

Rex.zone is a platform that supports AI/ML training workflows by connecting teams to data labeling, RLHF evaluation, and quality operations. Projects may include NLP labeling, named entity recognition, prompt-response grading, safety policy enforcement, and computer vision annotation. You will collaborate asynchronously with cross-functional stakeholders to maintain training data quality and ensure consistent labeling decisions across batches.

What You Will Do

You will produce and validate labeled datasets for LLM training pipelines and model evaluation. Typical work includes prompt evaluation, RLHF preference ranking, rubric-based scoring, QA evaluation, and dispute resolution using annotation guidelines. You may label text for named entity recognition, classify intent and topic, apply content safety labeling, or annotate images and video for computer vision annotation tasks. You will document edge cases, track error patterns, and propose guideline updates that drive model performance improvement.

Key Responsibilities

You will: (1) label and review training data using project rubrics and taxonomies, (2) perform QA evaluation and inter-annotator agreement checks, (3) execute RLHF-style comparisons and ranking tasks, (4) conduct prompt evaluation for helpfulness, correctness, and safety, (5) escalate ambiguous samples and propose clarifications to annotation guidelines compliance, (6) maintain throughput while meeting quality thresholds for training data quality, (7) contribute to auditing workflows for content safety labeling, and (8) support dataset versioning and error analysis for model performance improvement.

Required Qualifications

Mid-Senior experience supporting data annotation, QA, evaluation, or production operations in AI/ML. Demonstrated ability to follow detailed rubrics, maintain annotation guidelines compliance, and deliver consistent decisions at scale. Familiarity with LLM evaluation concepts (rubrics, preference ranking, prompt-response grading) and training data quality practices. Strong written communication for documenting edge cases, QA findings, and guideline feedback.

Preferred Qualifications

Experience with RLHF workflows, safety policy enforcement, and content safety labeling. Exposure to named entity recognition, taxonomy design, or ontology management. Experience with computer vision annotation (bounding boxes, polygons, segmentation) and multimodal evaluation. Familiarity with error analysis, sampling strategies, and basic experimentation to validate model performance improvement. Background working with AI labs, tech startups, BPOs, or annotation vendors.

Quality Standards And Workflow

You will work within defined QA evaluation gates, using spot checks, calibration rounds, and adjudication to keep training data quality high. The workflow emphasizes annotation guidelines compliance, clear rationales for decisions, and measurable targets (accuracy, consistency, turnaround time). You will participate in calibration sessions to reduce disagreement and improve labeling consistency across the team.

Work Arrangement

Remote, full-time role. Work is delivered through Rex.zone project systems with asynchronous collaboration and periodic calibration. This page targets search modifiers and candidate intent, including remote, full-time, contract, freelance, entry-level, and senior pathways; domain types like NLP, computer vision, content safety, and LLM training; and employer types such as AI labs, tech startups, BPOs, and annotation vendors.

How To Apply

Explore available AI data labeling jobs on Rex.zone and apply to matching projects. Your application should highlight experience with data labeling, QA evaluation, prompt evaluation, RLHF tasks, and any domain expertise in NLP, named entity recognition, computer vision annotation, or content safety labeling. Include examples of annotation guidelines compliance and training data quality improvements you have delivered.

Frequently Asked Questions

Q: What are AI data labeling jobs Canada on Rex.zone?
These are roles focused on producing and evaluating labeled training data used for AI/ML systems, including LLM training pipelines, RLHF evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling.
Q: Is this role remote and full-time?
Yes. The job is explicitly Remote and FULL_TIME, with structured workflows and quality targets.
Q: What types of tasks will I complete?
Common tasks include data labeling, rubric-based scoring, QA evaluation, prompt-response grading, RLHF preference ranking, NER tagging, and computer vision annotation, plus documentation of edge cases for annotation guidelines compliance.
Q: Do I need experience with RLHF or LLM evaluation?
It is strongly preferred. The role includes RLHF-style ranking and large language model evaluation, and familiarity with training data quality and model performance improvement practices is important.
Q: What skills are most important for success?
AI data labeling, data annotation, QA evaluation, annotation guidelines compliance, prompt evaluation, RLHF, training data quality, named entity recognition, computer vision annotation, and content safety labeling.
Q: What kinds of employers use this work?
Projects may support AI labs, tech startups, BPOs, and annotation vendors that require scalable data operations and consistent evaluation workflows.
Q: Are there other work types like contract or freelance?
This posting is FULL_TIME, but the page also covers common search modifiers such as contract and freelance to help candidates navigate related opportunities on Rex.zone.
Q: How is quality measured?
Quality is measured through QA evaluation, calibration rounds, adjudication, and consistency metrics that ensure training data quality and annotation guidelines compliance across batches.

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