Remote Data Annotator Jobs Toronto

About Rex.zone Rex.zone is hiring Toronto-based candidates for full-time remote data annotation and data labeling work that improves training data quality for AI/ML systems. You will support real-world LLM training pipelines through evaluation, QA, and careful rubric-driven judgments. About The Role As a Remote Data Annotator, you will create and evaluate labeled datasets used in large language model evaluation, RLHF workflows, NLP tasks (e.g., named entity recognition), computer vision annotation, and content safety labeling. You will follow strict annotation guidelines compliance, document edge cases, and collaborate asynchronously to improve model performance outcomes. Key Responsibilities Produce high-accuracy data labeling for text, image, and mixed-modality tasks Execute RLHF comparisons, preference judgments, response scoring, and rationale capture Perform prompt evaluation and rubric grading for helpfulness, correctness, and policy adherence Complete NLP annotations such as named entity recognition, classification, and entity linking using defined ontologies Support computer vision annotation including bounding boxes, polygons, segmentation masks, and attribute tagging Conduct content safety labeling across harassment, self-harm, sexual content, violence, and sensitive traits Run QA evaluation using gold sets, spot checks, reviewer audits, inter-annotator agreement, and defect taxonomies Report ambiguous examples, escalate edge cases, and propose guideline clarifications to reduce label noise Required Qualifications Mid-Senior experience in data annotation, data labeling, QA evaluation, or LLM evaluation Ability to interpret detailed rubrics and maintain consistent decision-making Strong written communication for edge-case documentation and rationale writing Comfort working with structured taxonomies (NER, content safety, prompt evaluation) Reliability in meeting throughput and quality targets in a remote environment Tools & Quality Standards You will work in web-based labeling platforms and evaluation consoles using versioned guidelines and task queues. Quality is measured through sampling, consensus review, inter-annotator agreement checks, and defect tagging. You must be able to handle potentially sensitive content and follow confidentiality requirements. How To Apply Apply via Rex.zone by submitting your profile and completing any required screening or calibration tasks. Keep your availability and domain strengths (NLP, computer vision, content safety, RLHF) up to date for better project matching.

Create and evaluate labeled datasets for LLM training, including RLHF workflows, NLP tasks, and computer vision annotation. Perform quality assurance through rubric grading, response scoring, and the documentation of edge cases to improve model performance.

Requires mid-senior level experience in data annotation, QA evaluation, or LLM evaluation. Candidates must be able to interpret detailed rubrics, maintain consistent decision-making, and work reliably in a remote environment.

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

• Data Annotation
• Data Labeling
• RLHF
• NLP
• Computer Vision
• Content Safety Labeling
• QA Evaluation
• Named Entity Recognition
• Prompt Evaluation
• Rubric Grading
• Entity Linking
• Bounding Boxes
• Segmentation Masks
• Attribute Tagging
• Written Communication
• Taxonomy Management

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

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, Toronto, Ontario, Canada

Niveau d’expérience

Mid-Senior level