Senior Data Annotator Jobs in Denver

Senior data annotator jobs in Denver focus on producing high-quality labeled datasets that power modern AI/ML systems on Rex.zone. In this remote, full-time role, you will deliver training data quality through data labeling, RLHF evaluation, prompt evaluation, and QA evaluation across NLP and computer vision workflows. Your work will support large language model evaluation, model performance improvement, and safe, reliable AI behavior by following annotation guidelines compliance, resolving edge cases, and calibrating to project rubrics. Rex.zone connects skilled annotators with AI labs, tech startups, and annotation vendors to help build LLM training pipelines at scale.

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LinkedIn Job Metadata

Title: Senior Data Annotator Jobs in Denver | 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 annotation, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

As a Senior Data Annotator, you will label and evaluate multimodal training data used in AI/ML products, including large language model evaluation and computer vision annotation. You will apply annotation guidelines compliance, document edge cases, and perform QA evaluation to ensure training data quality and consistency. Projects may include RLHF, prompt evaluation, named entity recognition, content safety labeling, and dataset auditing to drive model performance improvement in production-grade LLM training pipelines.

What You Will Do

You will produce and review high-quality data labeling outputs across text, image, and conversational datasets; perform RLHF ranking and preference labeling for large language model evaluation; execute prompt evaluation and response grading using rubrics; apply named entity recognition and span labeling with clear boundary rules; complete computer vision annotation tasks (bounding boxes, polygons, keypoints) when needed; run QA evaluation, error categorization, and spot checks to improve training data quality; escalate ambiguous cases, write edge-case notes, and propose rubric clarifications; collaborate with project leads on calibration sessions to maintain inter-annotator agreement.

Required Qualifications

You have prior experience in data annotation or data labeling for AI/ML systems; strong attention to detail with consistent annotation guidelines compliance; ability to follow rubrics for RLHF and prompt evaluation; familiarity with QA evaluation workflows and training data quality concepts; comfort working with NLP tasks such as named entity recognition and text classification; ability to communicate edge cases clearly and document decisions; availability for full-time remote work aligned to project schedules.

Preferred Qualifications

Experience supporting large language model evaluation and LLM training pipelines; experience with content safety labeling, policy-aware labeling, or safety taxonomies; exposure to computer vision annotation tools and quality checks; experience mentoring annotators or serving as a quality champion; experience working with AI labs, tech startups, BPOs, or annotation vendors on high-volume datasets.

Tools and Workflow

You will work in web-based annotation platforms, follow task-specific rubrics, and complete calibration rounds to maintain consistent labeling. Quality is measured through training data quality metrics, review outcomes, and QA evaluation audits. You will contribute to model performance improvement by reducing labeling variance, improving guideline clarity, and catching systematic errors early.

Compensation and Work Arrangement

This is a remote, full-time role. Compensation is paid yearly in USD within the listed salary range, depending on project scope, complexity, and quality expectations. Rex.zone provides access to multiple project types including NLP, computer vision, RLHF, and content safety labeling.

How to Apply on Rex.zone

Apply through Rex.zone by completing your profile, selecting Senior Data Annotator roles, and finishing any required screening tasks. Emphasize experience in data annotation, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to match project requirements.

Frequently Asked Questions

  • Q: Are these senior data annotator jobs in Denver remote?

    Yes. The role is explicitly Remote and available to candidates in the US, including Denver, with project schedules handled online through Rex.zone.

  • Q: What does a Senior Data Annotator do day to day?

    Typical work includes data labeling and data annotation, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, and documenting edge cases to maintain training data quality for LLM training pipelines.

  • Q: What AI domains are covered?

    Projects commonly span NLP, large language model evaluation, computer vision annotation, and content safety labeling, depending on the employer and dataset type.

  • Q: Is this full-time or contract?

    This posting is for FULL_TIME employment. Rex.zone may also host contract or freelance projects, but this role remains full-time as specified.

  • Q: What skills should I highlight to be competitive?

    Highlight training data quality, annotation guidelines compliance, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and consistent decision-making on ambiguous examples.

  • Q: How is quality measured?

    Quality is assessed through QA evaluation audits, calibration results, guideline adherence, error rates, and consistency checks that correlate with model performance improvement.

  • Q: Who hires through Rex.zone for this type of role?

    Employers can include AI labs, tech startups, BPOs, and annotation vendors seeking reliable contributors for large language model evaluation and broader AI/ML training workflows.

  • Q: What salary range is listed for this role?

    The listed yearly salary range is 63360 to 126720 USD, depending on scope and requirements.

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