Remote Data Annotator Jobs in Montreal

Remote Data Annotator jobs in Montreal on Rex.zone focus on creating, labeling, and evaluating training data used in modern AI/ML systems. As a Remote Data Annotator, you will apply annotation guidelines to text, images, audio, and multi-turn conversations to improve large language model evaluation, RLHF feedback quality, and training data quality across NLP, computer vision annotation, and content safety labeling workflows. You will support real-world LLM training pipelines with QA evaluation, prompt evaluation, and named entity recognition tasks, helping teams drive model performance improvement while meeting accuracy, consistency, and compliance standards. Explore full-time remote roles and collaborate with AI labs, tech startups, and annotation vendors through Rex.zone.

Remote Data Annotator Jobs in Montreal

About the Role

You will perform high-accuracy data annotation and evaluation to support AI/ML model development. This includes labeling and reviewing text, image, and audio datasets; scoring model outputs for helpfulness, harmlessness, and factuality; and producing structured feedback used in RLHF and supervised fine-tuning. You will work within defined annotation guidelines, track edge cases, and communicate ambiguities to improve taxonomy, rubrics, and training data quality.

What You Will Work On

Core workflows include: (1) LLM evaluation and prompt evaluation for instruction-following, reasoning quality, and safety, (2) RLHF-style preference labeling and critique writing, (3) NLP labeling such as named entity recognition, intent classification, and sentiment, (4) computer vision annotation including bounding boxes, polygons, and segmentation QA, (5) content safety labeling for policy categories and risk severity, and (6) QA evaluation using sampling plans, inter-annotator agreement checks, and corrective actions.

Responsibilities

You will: apply labeling policies consistently; follow annotation guidelines compliance requirements; document rationale for tricky decisions; perform peer review and adjudication; flag dataset issues like duplicates, leakage, or label noise; measure and improve annotation quality via QA checklists; support model performance improvement by producing high-signal feedback; and maintain confidentiality and data handling standards for sensitive content.

Required Qualifications

Mid-Senior experience in data labeling, data annotation, or QA evaluation; ability to interpret detailed rubrics and maintain high consistency; comfort working with structured labeling tools; strong written reasoning for critique-based evaluations; familiarity with NLP, large language model evaluation, or content moderation taxonomies; and ability to operate in a remote, metrics-driven environment.

Preferred Qualifications

Experience with RLHF workflows, preference ranking, or rubric-based scoring; exposure to computer vision annotation (bounding boxes, segmentation); understanding of training data quality concepts like bias, representativeness, and label noise; experience improving guidelines through edge case analysis; and prior work with AI labs, tech startups, BPOs, or annotation vendors supporting LLM training pipelines.

Quality and Performance Expectations

Success is measured through annotation accuracy, consistency, throughput, and audit readiness. You will be expected to pass calibration tasks, maintain strong inter-annotator agreement, and demonstrate reliable decision-making on ambiguous cases. You will contribute to continuous improvement by proposing guideline clarifications that reduce disagreement and increase training data quality.

Role Types and Modifiers (Search Intent Coverage)

This page targets common job modifiers and pathways, including remote, full-time, contract, freelance, entry-level, and senior tracks. Work may span NLP labeling, computer vision annotation, content safety labeling, and LLM evaluation depending on project needs, with opportunities supporting AI labs, tech startups, BPOs, and specialized annotation vendors.

How to Apply on Rex.zone

Use Rex.zone to review requirements, confirm remote eligibility, and submit your application for Remote Data Annotator jobs aligned to Montreal search intent. Prepare examples of guideline-based decisions, QA checks you have performed, and any experience with RLHF, prompt evaluation, or model output grading.

Frequently Asked Questions

Q: What does a Remote Data Annotator do in AI/ML?
A Remote Data Annotator creates labeled datasets and evaluation signals used to train and test AI models. Typical work includes data labeling, QA evaluation, prompt evaluation, and LLM evaluation tasks that improve training data quality and model performance improvement.
Q: Is this role focused on RLHF?
Many projects include RLHF-style preference labeling, rubric scoring, and critique writing to guide large language models. Specific task mix can also include named entity recognition, computer vision annotation, and content safety labeling.
Q: Are these remote data annotator jobs full-time?
Yes, this posting is for FULL_TIME remote roles. Rex.zone may also list contract or freelance roles, but the metadata for this job remains full-time and remote.
Q: What skills matter most for remote annotation work?
Strong annotation guidelines compliance, careful reasoning, consistency, and QA habits matter most. Useful domain skills include data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling.
Q: Do I need an engineering background if the job function is Engineering?
You do not need to write production code for most annotation roles, but you should be comfortable working with structured tools, taxonomies, and quality metrics. The Engineering function reflects alignment with AI/ML development workflows and evaluation pipelines.
Q: How does Rex.zone fit into the application process?
Rex.zone is the platform where you discover and apply to remote data annotator jobs. It centralizes role details, requirements, and workflow expectations tied to AI training pipelines.

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