[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-labeling-jobs-vancouver":3},{"Ques":4,"Slug":28,"Header":29,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"They are Remote roles that can be performed from Vancouver, focused on data labeling and data annotation tasks used in AI\u002FML training workflows. Work may include NLP labeling (e.g., named entity recognition), RLHF preference ranking, prompt evaluation, computer vision annotation, and content safety labeling.","What are remote data labeling jobs in Vancouver?",{"A":11,"Q":12},"This posting is FULL_TIME. However, remote data labeling work in the market often also appears as contract or freelance, and Rex.zone may list those options depending on project needs.","Is this role full-time or contract\u002Ffreelance?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) is a training approach where humans rank or rate model outputs to guide model alignment. In practice, it looks like preference ranking, rubric grading, and prompt evaluation tasks that improve large language model evaluation and model performance improvement.","What is RLHF and why is it included in data labeling?",{"A":17,"Q":18},"QA evaluation is the quality assurance process for labeled data, including audits, sampling checks, guideline compliance reviews, and error categorization. It improves training data quality and reduces inconsistencies that can degrade model performance.","What does QA evaluation mean in data annotation work?",{"A":20,"Q":21},"Highlight data labeling, data annotation, annotation guidelines compliance, QA evaluation, prompt evaluation, RLHF, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines.","What skills should I highlight for remote data labeling jobs?",{"A":23,"Q":24},"AI labs, tech startups, enterprise AI teams, BPOs, and specialized annotation vendors hire for data labeling and LLM evaluation, often across NLP, computer vision, and content safety domains.","What kinds of employers hire for remote data labeling?",{"A":26,"Q":27},"Yes. Entry-level roles focus on executing labeling tasks accurately, while senior roles may include rubric design, QA evaluation leadership, resolving edge cases, and improving annotation workflows. This posting targets Mid-Senior level experience.","Are there entry-level or senior paths for data labeling work?","remote-data-labeling-jobs-vancouver",{"desc":30,"title":31,"content":32},"Remote data labeling jobs in Vancouver support real AI\u002FML training workflows on Rex.zone, turning raw text, images, audio, and video into high-quality labeled datasets for large language models and computer vision systems. As a Data Labeling Specialist, you will follow annotation guidelines, complete RLHF and prompt evaluation tasks, perform QA evaluation to improve training data quality, and help drive model performance improvement across NLP, content safety labeling, and multimodal LLM training pipelines. This page is designed for candidates seeking full-time remote roles, plus common alternatives like contract, freelance, entry-level, and senior paths across AI labs, tech startups, BPOs, and annotation vendors.","Remote Data Labeling Jobs in Vancouver",[33,36,39,42,45,48,51,54],{"h2":34,"desc":35},"Job Opening: Remote Data Labeling Specialist (Vancouver)","Title: Remote Data Labeling Specialist (Vancouver)\nDate: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: data labeling, data annotation, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":37,"desc":38},"About the Role","You will deliver consistent, policy-aligned annotations used to train and evaluate AI models. Typical work includes data labeling for text classification, named entity recognition (NER), summarization quality checks, RLHF preference ranking, prompt evaluation, and content safety labeling. You will also support QA evaluation by reviewing labeled samples for accuracy, guideline compliance, ambiguity resolution, and edge case handling, helping improve training data quality and downstream model performance.",{"h2":40,"desc":41},"Key Responsibilities","You will:\n- Apply annotation guidelines compliance across NLP and computer vision tasks.\n- Perform RLHF rankings and evaluation to align model behavior with human preferences.\n- Execute prompt evaluation and response quality scoring for LLM evaluation.\n- Conduct QA evaluation, sampling audits, and error categorization to reduce label noise.\n- Label content safety categories (e.g., harassment, self-harm, hate) using policy taxonomies.\n- Collaborate with project leads to refine rubrics, resolve disagreements, and document decisions.\n- Track productivity and accuracy metrics to support model performance improvement.",{"h2":43,"desc":44},"Workstreams You May Support","Depending on project needs, you may work on:\n- NLP data labeling: NER, intent classification, sentiment, topic labeling, summarization evaluation.\n- LLM training pipelines: instruction-following evaluation, pairwise preference ranking, rubric-based grading.\n- Computer vision annotation: bounding boxes, polygons, keypoints, segmentation, OCR validation.\n- Content safety labeling: policy-based moderation labels and severity scoring.\n- Multimodal tasks: text-image relevance, caption validation, VQA-style evaluation.",{"h2":46,"desc":47},"Required Qualifications","- Experience applying structured guidelines to data annotation or QA evaluation work.\n- Strong written communication for consistent label rationale and disagreement resolution.\n- Comfort working with ambiguous examples, edge cases, and evolving rubrics.\n- Familiarity with NLP, RLHF, or LLM evaluation concepts (training data quality, preference ranking, prompt evaluation).\n- Ability to maintain accuracy at production scale and meet full-time delivery expectations.",{"h2":49,"desc":50},"Preferred Qualifications","- Prior work with annotation platforms and audit workflows.\n- Exposure to computer vision annotation tools (boxes, polygons, segmentation).\n- Experience with content safety labeling or policy taxonomy interpretation.\n- Experience documenting guidelines, building QA checklists, or running inter-annotator agreement checks.",{"h2":52,"desc":53},"Remote Work Notes (Vancouver Applicants)","This is a Remote role. You can be based in Vancouver while working with distributed teams. Projects may require overlap with US business hours, and task availability can vary by domain (NLP, computer vision, content safety, LLM training pipelines).",{"h2":55,"desc":56},"How to Apply on Rex.zone","Apply through Rex.zone to be considered for remote data labeling jobs aligned to your skills and domain interests. You may be asked to complete a short qualification task covering annotation guidelines compliance, QA evaluation checks, or RLHF\u002Fprompt evaluation scenarios.","AI Data Operations"]