Remote Data Annotation Jobs in Denver

Remote data annotation jobs in Denver are full-time roles focused on data labeling and AI/ML evaluation to improve large language model training pipelines on Rex.zone. You will apply annotation guidelines compliance across text, images, and safety tasks, support RLHF ranking and prompt evaluation, and run QA evaluation to ensure training data quality. This work connects directly to model performance improvement for AI labs, tech startups, and annotation vendors, with projects spanning NLP, computer vision annotation, named entity recognition, and content safety labeling. Explore and apply through Rex.zone to match your skills to active remote programs and long-term LLM data operations work.

Remote Data Annotation Jobs in Denver

About the Role

You will deliver high-accuracy labeled datasets and evaluation judgments used in AI/ML training workflows. Typical work includes text classification, entity spans for NLP, prompt-response evaluation, RLHF preference ranking, and computer vision annotation (bounding boxes, polygons, segmentation) depending on project needs. You will follow detailed annotation guidelines, document edge cases, and collaborate with QA to improve label consistency and training data quality for downstream model performance improvement.

Key Responsibilities

Perform data labeling for NLP tasks such as named entity recognition, sentiment, intent, and taxonomy-based classification. Complete RLHF-style ranking and preference labeling to support large language model evaluation and alignment. Run prompt evaluation and response grading for accuracy, helpfulness, policy compliance, and reasoning quality. Execute content safety labeling for hate/harassment, self-harm, sexual content, and violence categories using client rubrics. Annotate computer vision data when needed (boxes, polygons, keypoints, segmentation masks) with strict quality thresholds. Apply annotation guidelines compliance, flag ambiguous items, and propose guideline clarifications to reduce disagreement. Conduct QA evaluation (self-check and peer review), track error types, and drive continuous quality improvements. Maintain secure handling of sensitive data and adhere to project confidentiality and platform policies.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, QA review, or AI/ML evaluation workflows. Strong written communication for documenting rationales, edge cases, and guideline interpretations. Demonstrated ability to follow detailed rubrics with high consistency and measured quality. Comfort working with web-based annotation tools and spreadsheet-style task tracking. Understanding of LLM training pipelines concepts such as supervised fine-tuning data, RLHF, and evaluation sets.

Preferred Qualifications

Experience with NLP evaluation, NER schema design exposure, or linguistic annotation conventions. Experience with computer vision annotation types (bounding boxes, polygons, segmentation) and quality checks. Prior work on content safety labeling and policy-based moderation taxonomies. Familiarity with inter-annotator agreement, calibration sessions, and error analysis. Experience supporting AI labs, tech startups, BPOs, or annotation vendors in production data operations.

Tools and Workflows You May Use

Annotation platforms for text, audio, and computer vision tasks (project-dependent). Rubric-driven evaluation forms for prompt evaluation and RLHF ranking tasks. QA sampling, adjudication queues, and disagreement analysis to improve training data quality. Dataset auditing checklists and reporting templates for model performance improvement insights.

Work Location and Schedule

Remote Type: Remote (Denver-based candidates welcomed; work is performed remotely within the US). Employment Type: FULL_TIME with sustained throughput and quality expectations. Projects may be long-running or time-boxed; some workflows resemble contract or freelance-style task streams, but this posting is FULL_TIME as listed.

How to Apply on Rex.zone

Create or update your Rex.zone profile with your annotation domain strengths (NLP, LLM evaluation, content safety, or computer vision annotation). Highlight prior QA evaluation, guideline compliance, and calibration experience with measurable outcomes. Apply to the Denver-targeted remote data annotation role and complete any required screening tasks.

Frequently Asked Questions

Q: What are remote data annotation jobs in Denver?
They are remote, US-based roles where you label and evaluate training data used to build and improve AI systems. Work commonly includes data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling, accessed and managed through Rex.zone.
Q: Is this role truly remote?
Yes. Remote Type is Remote and the work is performed remotely within the US. Denver is included as the target location modifier for candidates searching from Denver.
Q: What kinds of AI projects do annotators support?
Projects often support LLM training pipelines, including supervised fine-tuning datasets, evaluation sets, and RLHF alignment data. Domain areas can include NLP, computer vision, and content safety for AI labs, tech startups, BPOs, and annotation vendors.
Q: What is RLHF and how does it relate to data annotation?
RLHF (Reinforcement Learning from Human Feedback) uses human judgments—often preference rankings and rubric-based ratings—to improve model behavior. In annotation workflows, you provide pairwise or listwise rankings, correctness and safety ratings, and rationales that become training signals or evaluation benchmarks.
Q: What does QA evaluation mean in this job?
QA evaluation includes reviewing labels for accuracy and consistency, applying annotation guidelines compliance checks, participating in calibration, and documenting recurring error patterns to improve training data quality and downstream model performance improvement.
Q: Do I need computer vision experience to qualify?
Not always. Some programs are NLP-heavy (e.g., named entity recognition and prompt evaluation), while others include computer vision annotation. If you have CV annotation experience, it can expand the types of projects you can support.
Q: Is this entry-level or senior?
The Experience Level for this posting is Mid-Senior. Candidates are expected to handle complex rubrics, edge cases, and quality standards with minimal supervision.
Q: How does Rex.zone fit into the application process?
Rex.zone is the platform context for discovering and applying to remote annotation and evaluation opportunities. Your Rex.zone profile and screening results help match you to projects aligned with your skills and domain strengths.

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