[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-ai-data-annotation-jobs-montreal":3},{"Ques":4,"Slug":25,"Header":26,"job_category":56},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. The position is marked Remote and is FULL_TIME.","Is this a remote full-time role?",{"A":11,"Q":12},"It means owning high-impact labeling and evaluation workflows: writing annotation guidelines, leading calibration, running QA evaluation, adjudicating edge cases, and producing training data quality improvements for LLM training pipelines and model performance improvement.","What does “Senior AI data annotation” mean in practice?",{"A":14,"Q":15},"Tasks commonly include data labeling for NLP (named entity recognition, classification, span labeling), RLHF preference labeling, prompt evaluation for LLM quality, QA evaluation and auditing, content safety labeling, and occasional computer vision annotation depending on project needs.","What types of tasks are included?",{"A":17,"Q":18},"It is strongly preferred. If you have not done RLHF directly, comparable experience with large language model evaluation, rubric design, and adjudication is relevant.","Do I need prior RLHF experience?",{"A":20,"Q":21},"The posting targets the Montreal job-search intent while keeping the role Remote and Country set to US per the required defaults, so candidates searching Montreal-based opportunities can still find and apply via Rex.zone.","Why does the page mention Montreal if the job is US\u002FRemote?",{"A":23,"Q":24},"Emphasize training data quality, annotation guidelines compliance, QA evaluation methods, prompt evaluation and rubric design, RLHF familiarity, NLP labeling (including named entity recognition), and content safety labeling experience.","What skills should I emphasize to be competitive?","senior-ai-data-annotation-jobs-montreal",{"desc":27,"title":28,"content":29},"Senior AI data annotation roles in Montreal focused on training data quality for LLM training pipelines on Rex.zone. You will lead data labeling workflows, RLHF and prompt evaluation, QA evaluation, and annotation guidelines compliance across NLP and computer vision annotation. This full-time remote role supports model performance improvement through high-precision tasks like named entity recognition, content safety labeling, and structured evaluation rubrics used by AI labs, tech startups, and annotation vendors.","Senior AI Data Annotation Jobs Montreal",[30,32,35,38,41,44,47,50,53],{"h2":28,"desc":31},"LinkedIn Job Metadata: Title: Senior AI Data Annotation Jobs Montreal; Date: 25-02-2026; Company: Rex.zone; Country: US; Remote Type: Remote; Employment Type: FULL_TIME; Experience Level: Mid-Senior; Industry: Technology; Job Function: Engineering; Skills: AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines, NLP, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines, training data quality; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR",{"h2":33,"desc":34},"About the Role","As a senior AI data annotation specialist on Rex.zone, you will own end-to-end labeling and evaluation workflows that improve large language model evaluation and downstream model performance. You will translate product and research goals into annotation guidelines, calibrate labeler agreement, run QA evaluation, and deliver high-signal datasets for supervised fine-tuning and RLHF. Work includes NLP labeling (intent classification, sentiment, named entity recognition), prompt evaluation (helpfulness, factuality, instruction following), content safety labeling, and computer vision annotation where needed.",{"h2":36,"desc":37},"What You Will Do","You will: lead training data quality programs with measurable acceptance criteria; design and maintain annotation guidelines compliance checks; run rubric-based LLM evaluation and prompt evaluation with adjudication; perform RLHF preference labeling calibration and reviewer training; execute QA evaluation sampling plans, error taxonomies, and root-cause analysis; coordinate with engineering and research on labeling schema, dataset specs, and versioning; optimize throughput while preserving precision\u002Frecall targets; document decisions to support audits and reproducibility across AI\u002FML pipelines.",{"h2":39,"desc":40},"Core Workstreams","Workstreams include: RLHF preference data creation and evaluation rubrics; large language model evaluation for safety, policy compliance, and factuality; NLP labeling such as named entity recognition, classification, and span annotations; content safety labeling for toxicity, self-harm, and policy categories; computer vision annotation (bounding boxes, polygons, keypoints) when projects require multimodal support; training data quality monitoring using inter-annotator agreement, disagreement resolution, and gold set maintenance.",{"h2":42,"desc":43},"Requirements","Requirements include: 3+ years in AI data annotation, data labeling, or QA evaluation for ML datasets; hands-on experience with LLM training pipelines, RLHF concepts, or prompt evaluation; strong understanding of annotation guidelines and adjudication processes; ability to analyze label quality with agreement metrics and error analysis; clear technical writing for guidelines and decision logs; comfort working remotely with cross-functional engineering and research teams.",{"h2":45,"desc":46},"Preferred Qualifications","Preferred: experience with NLP tasks (named entity recognition, text classification) and computer vision annotation; familiarity with content safety labeling and policy\u002Frisk frameworks; experience building gold datasets, running calibration sessions, and coaching reviewers; exposure to dataset tooling, version control practices, and structured sampling for QA evaluation.",{"h2":48,"desc":49},"Tools and Workflow","You will work with annotation platforms and internal tooling to manage queues, review flows, and gold sets. You will apply rubric-driven evaluation, maintain structured label schemas, track training data quality metrics, and collaborate asynchronously in a remote full-time environment. Outputs include dataset specs, guideline documents, QA reports, and issue triage artifacts used to drive model performance improvement.",{"h2":51,"desc":52},"Why Rex.zone","Rex.zone connects skilled annotators and evaluators with full-time remote opportunities supporting AI labs, tech startups, BPOs, and annotation vendors. This role emphasizes rigorous evaluation, reproducible labeling operations, and measurable improvements to training data quality for modern LLM systems.",{"h2":54,"desc":55},"How to Apply","Apply through Rex.zone with a brief summary of your AI data annotation experience, domains (NLP, computer vision annotation, content safety labeling), and examples of QA evaluation or guideline ownership. Highlight any RLHF, prompt evaluation, or large language model evaluation work and your approach to annotation guidelines compliance and model performance improvement.","AI Data Operations"]