Senior Data Labeling Jobs in Vancouver

Senior data labeling roles power AI/ML training workflows by producing high-quality training data for LLMs and computer vision models through data annotation, RLHF preference ranking, QA evaluation, and guideline-driven review. On Rex.zone, these remote full-time roles support model performance improvement via training data quality, annotation guidelines compliance, prompt evaluation, named entity recognition, and content safety labeling across real production pipelines for AI labs, tech startups, and annotation vendors.

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Senior Data Labeling Jobs in Vancouver

Title: Senior Data Labeling Specialist (Vancouver) 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: Data Labeling, Data Annotation, RLHF, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Training Pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will lead and execute senior-level data labeling and review work that improves model performance for large language models and vision systems. You will annotate and audit datasets, run QA evaluation against annotation guidelines, perform RLHF preference ranking for response quality, and deliver structured feedback to improve training data quality. Work includes prompt evaluation, named entity recognition, content safety labeling, and computer vision annotation (bounding boxes, polygons, segmentation) depending on project needs. You will collaborate with engineers and model stakeholders to resolve ambiguity, calibrate decisions, and maintain consistent annotation guidelines compliance across distributed labeling teams.

What You Will Do

Core workflows you will own include end-to-end labeling execution, quality auditing, and escalation handling for ambiguous edge cases in NLP and CV tasks. You will apply taxonomy definitions, maintain decision logs, and measure inter-annotator agreement to stabilize training signals. You will support RLHF and evaluation pipelines by ranking outputs, identifying failure modes, and tagging safety or policy violations. You will help define gold-standard sets, build rubrics for QA evaluation, and contribute to continuous improvement that increases dataset reliability and model performance improvement.

Projects You May Support

Projects may include large language model evaluation, instruction-following and helpfulness/harmlessness ranking (RLHF), prompt evaluation for conversation quality, and content safety labeling for policy enforcement. NLP datasets can include named entity recognition, intent classification, summarization evaluation, and factuality checks. Computer vision annotation projects can include bounding boxes, keypoints, polygons, and semantic segmentation for real-world imagery. Employer contexts can include AI labs, tech startups, BPOs, and annotation vendors supporting enterprise AI programs.

Requirements

You have strong experience in data labeling or data annotation with documented QA evaluation responsibility, plus the ability to interpret complex annotation guidelines and apply consistent judgment. You can communicate edge cases clearly, maintain high throughput without sacrificing quality, and work independently in a remote environment. Familiarity with RLHF, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling is expected for senior scope. You are comfortable working with ambiguous natural language, measuring quality with rubrics, and iterating on guidelines to improve training data quality.

Nice to Have

Experience with LLM training pipelines and evaluation harnesses, building gold datasets, or running calibration sessions. Exposure to multilingual annotation, domain-specific taxonomies (health, finance, legal), and safety policy frameworks. Prior work with annotation vendors, BPO teams, or cross-functional stakeholders (engineering, product, research). Understanding of common model failure patterns and how labeling choices affect downstream model performance improvement.

How to Apply on Rex.zone

Apply directly via Rex.zone and include examples of annotation guideline interpretation, QA evaluation outcomes, and any RLHF or prompt evaluation work. Highlight the domains you have labeled (NLP, computer vision, content safety) and the methods you used to ensure annotation guidelines compliance and training data quality. If you have led audits or built rubrics, include concrete metrics such as agreement rates, defect reductions, or throughput with quality targets.

Frequently Asked Questions

  • Q: Are these senior data labeling jobs in Vancouver remote?

    Yes. The roles on this page are explicitly marked Remote and are designed for distributed teams supporting AI/ML training workflows.

  • Q: What does “senior” mean in data labeling?

    Senior scope typically includes ownership of training data quality, QA evaluation, edge-case escalation, rubric creation, and helping improve annotation guidelines compliance across a team, not only completing labeling tasks.

  • Q: Do these roles involve RLHF?

    Often, yes. Many senior data labeling roles support RLHF by doing preference ranking, response quality review, and prompt evaluation that feeds reinforcement learning and evaluation datasets.

  • Q: What domains are common for these roles?

    Common domains include NLP (named entity recognition, intent, summarization), computer vision annotation (boxes, polygons, segmentation), and content safety labeling for policy and risk categories in LLM outputs.

  • Q: Is this full-time employment?

    Yes. The job metadata specifies Employment Type: FULL_TIME, while acknowledging that related markets may include contract or freelance roles depending on project needs.

  • Q: What companies hire for senior data labeling work?

    Hiring contexts include AI labs, tech startups, enterprise ML teams, BPOs, and annotation vendors building or evaluating datasets for LLM training pipelines.

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