Senior Data Annotation Jobs in Montreal

Senior data annotation jobs in Montreal focus on creating and evaluating high-quality training data for AI/ML systems used in real-world products. At Rex.zone, you will support LLM training pipelines through data labeling, RLHF evaluation, prompt evaluation, and QA review that improve model performance and safety. This remote, full-time role connects annotation guidelines compliance, training data quality, and large language model evaluation across NLP, computer vision, and content safety labeling workflows. If you have experience leading annotation projects, auditing datasets, and driving model performance improvement with clear taxonomy design, explore and apply through Rex.zone.

Senior Data Annotation Jobs in Montreal

About the Role

You will lead and execute senior-level data annotation workflows that improve training data quality for AI/ML models. The work includes building and refining annotation guidelines, running calibration sessions, performing QA evaluation, and conducting error analysis to reduce label noise. You will support large language model evaluation tasks such as RLHF comparisons, preference ranking, prompt evaluation, and rubric-based scoring, plus NLP tasks like named entity recognition and text classification. You may also contribute to computer vision annotation (bounding boxes, polygons, keypoints) and content safety labeling for policy compliance. You will collaborate with engineering and model stakeholders to translate model failures into labeling tasks that drive measurable model performance improvement.

Key Responsibilities

Deliver high-accuracy data labeling and review across NLP, LLM evaluation, computer vision annotation, and content safety labeling; Design, maintain, and enforce annotation guidelines compliance and taxonomy standards; Lead QA evaluation workflows including sampling plans, inter-annotator agreement checks, and adjudication; Run RLHF task execution such as pairwise ranking, preference labeling, and consistency audits; Conduct prompt evaluation and rubric-based scoring aligned to model requirements; Perform dataset audits, ambiguity logging, and label error analysis to improve training data quality; Partner with engineering and ML stakeholders to define acceptance criteria and feedback loops into LLM training pipelines; Document decisions, edge cases, and escalation paths to reduce guideline drift; Support onboarding and calibration of annotators through examples, gold sets, and retraining plans; Track quality metrics and recommend process improvements that improve throughput without sacrificing accuracy.

Required Qualifications

Experience in senior data annotation or data labeling operations with demonstrated QA evaluation ownership; Strong understanding of annotation guidelines compliance, taxonomy design, and ambiguity resolution; Hands-on experience with LLM evaluation, RLHF, or prompt evaluation workflows; Familiarity with NLP tasks including named entity recognition, text classification, or sentiment labeling; Exposure to computer vision annotation methods (bounding boxes, polygons, segmentation) is preferred; Ability to perform error analysis and translate findings into actionable guideline updates; Strong written communication for edge-case documentation and reviewer feedback; Comfortable working in a remote, full-time environment with cross-functional stakeholders.

Nice-to-Have Experience

Experience supporting content safety labeling or policy-driven evaluation; Familiarity with inter-annotator agreement metrics and quality sampling methodologies; Experience with multilingual evaluation or locale-specific annotation guidelines; Experience collaborating with AI labs, tech startups, BPOs, or annotation vendors; Exposure to production data workflows, dataset versioning, and audit trails for training datasets.

Work Model and Job Modifiers

This role is Remote and FULL_TIME. If you are exploring other job types, Rex.zone may also list contract, freelance, and entry-level annotation roles depending on project needs. Domain work may span NLP, computer vision, content safety, and LLM training pipelines.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile, relevant annotation experience, and examples of QA evaluation or guideline development work. Highlight senior data annotation outcomes such as training data quality improvements, calibration leadership, and measurable reductions in labeling errors.

Frequently Asked Questions

Q: What are senior data annotation jobs in Montreal?
Senior data annotation jobs in Montreal typically involve leading annotation quality, defining guidelines, and executing high-complexity labeling and evaluation tasks for AI/ML training. Even when listed with a Montreal modifier, roles can be remote and may support global model training pipelines through Rex.zone.
Q: Is this role remote and full-time?
Yes. The Remote Type is Remote and the Employment Type is FULL_TIME, aligned with the job metadata shown under the job heading.
Q: What does RLHF work include for senior annotators?
RLHF tasks often include preference ranking, pairwise comparisons, rubric-based scoring, and consistency checks. Senior annotators also help refine instructions, adjudicate edge cases, and run QA evaluation to improve label reliability for LLM training pipelines.
Q: Which domains are covered: NLP, computer vision, or content safety?
The role can include NLP tasks such as named entity recognition, LLM evaluation tasks such as prompt evaluation and RLHF, computer vision annotation tasks like bounding boxes or segmentation, and content safety labeling aligned to policy requirements.
Q: What skills are most important for training data quality?
Strong annotation guidelines compliance, QA evaluation methods, ambiguity resolution, consistent labeling, and error analysis are critical. These directly impact training data quality and model performance improvement.
Q: Do you hire contract, freelance, or entry-level workers too?
This posting is for a full-time Mid-Senior role. Rex.zone may also feature contract, freelance, or entry-level roles depending on client demand and project timing.
Q: What types of employers use Rex.zone for data annotation roles?
Employers can include AI labs, technology companies, tech startups, BPOs, and annotation vendors seeking reliable data labeling and evaluation capacity for production AI systems.
Q: How will performance be evaluated in this role?
Performance is commonly measured through accuracy against gold standards, inter-annotator agreement, QA evaluation pass rates, guideline adherence, throughput, and demonstrated improvements to dataset consistency and model evaluation outcomes.

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