Senior Data Labeling Jobs in Denver

Senior data labeling jobs in Denver at Rex.zone focus on training data quality for AI/ML systems, including RLHF, LLM evaluation, and annotation workflows across NLP, computer vision, and content safety labeling. In this remote, full-time role, you will apply annotation guidelines compliance, run QA evaluation and prompt evaluation, and deliver high-precision labeled datasets that drive model performance improvement in real-world LLM training pipelines. If you’re seeking remote senior data labeling work connected to modern AI labs, tech startups, and annotation vendors, explore Rex.zone and apply to help improve large language model evaluation and production-scale data operations.

Job Heading: Senior Data Labeling Jobs in Denver

Title: Senior Data Labeling Specialist (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: Senior data labeling, RLHF evaluation, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, content safety labeling Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will lead and execute senior data labeling workflows to produce high-quality training data for AI/ML systems. This includes creating and refining annotation guidelines, running QA evaluation, supporting RLHF and preference ranking, and partnering with cross-functional teams to improve dataset quality for model performance improvement. Projects may span NLP (named entity recognition, text classification), LLM evaluation (prompt evaluation, response rating), computer vision annotation (bounding boxes, segmentation), and content safety labeling.

What You Will Do

Core responsibilities include: - Perform expert data labeling across text, image, and multimodal tasks aligned to production requirements. - Execute RLHF-style preference labeling, rubric-based scoring, and LLM evaluation to improve alignment and usefulness. - Apply annotation guidelines compliance checks; identify ambiguity, edge cases, and failure modes. - Run QA evaluation workflows (spot checks, double-pass review, inter-annotator agreement) to raise training data quality. - Deliver clear issue reports and suggested rubric updates to reduce label noise and improve consistency. - Support prompt evaluation and safety evaluation tasks for content safety labeling and policy adherence. - Collaborate with engineering and data operations on sampling strategies, task design, and measurement of model performance improvement.

Required Qualifications

You should have: - Mid-senior experience in data labeling, data annotation, or LLM evaluation in production or vendor settings. - Strong understanding of training data quality, label taxonomy design, and annotation guidelines compliance. - Experience with QA evaluation methods (audit plans, disagreement analysis, calibration sessions). - Familiarity with RLHF concepts, preference ranking, and rubric-driven evaluation. - Ability to communicate edge cases precisely and document decisions for consistent labeling. - Comfort working remotely in a full-time schedule with measurable output targets.

Preferred Qualifications

Nice to have: - Experience with named entity recognition, semantic similarity, summarization evaluation, or retrieval relevance grading. - Computer vision annotation exposure (polygons, instance segmentation, keypoints) or multimodal evaluation. - Content safety labeling experience (policy mapping, severity ratings, refusals/allowlists). - Experience working with AI labs, tech startups, BPOs, or annotation vendors and managing throughput vs. quality tradeoffs.

How Success Is Measured

Success metrics may include: - Training data quality improvements (lower error rates, higher agreement, fewer guideline violations). - Consistent annotation guidelines compliance across batches and edge cases. - QA evaluation outcomes (audit pass rate, reduced rework, stable calibration). - Demonstrated impact on model performance improvement for LLM training pipelines (via evaluation signals and error analysis feedback loops).

Work Arrangement

This is a Remote, FULL_TIME position based in the US market and aligned to senior data labeling jobs in Denver search intent. You will collaborate with distributed teams and contribute to scalable annotation operations supporting NLP, computer vision, RLHF, and content safety labeling.

Apply on Rex.zone

Apply through Rex.zone to be considered for senior data labeling opportunities aligned to LLM evaluation, RLHF evaluation, and QA evaluation. Ensure your application highlights training data quality experience, annotation guidelines compliance work, and any domain coverage across NLP, computer vision annotation, and content safety labeling.

Frequently Asked Questions

Q: Are these senior data labeling jobs in Denver remote or onsite?
These roles are explicitly Remote and designed for full-time remote delivery while aligning with Denver-based search intent and US hiring requirements.
Q: What types of data labeling tasks are included?
Tasks commonly include RLHF preference labeling, LLM evaluation and prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling depending on the project.
Q: What does “senior” mean for data labeling work?
Senior scope typically includes higher-complexity judgments, stronger annotation guidelines compliance, leading calibration/QA evaluation cycles, resolving edge cases, and contributing to training data quality improvements that impact model performance improvement.
Q: Is this role full-time or contract/freelance?
This posting is FULL_TIME. Rex.zone may also host contract or freelance data labeling roles, but this specific role is full-time.
Q: Which AI domains does this role support?
You may work across NLP, large language model evaluation, RLHF workflows, computer vision annotation, and content safety labeling depending on customer needs and pipeline priorities.
Q: What skills should I highlight to match the job intent?
Emphasize senior data labeling experience, training data quality, annotation guidelines compliance, QA evaluation methods, RLHF evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling.
Q: What employer types do these projects typically support?
Projects may support AI labs, tech startups, BPOs, and annotation vendors building and evaluating LLM training pipelines and production AI systems.
Q: How do I apply?
Apply via Rex.zone and include examples of annotation quality work, QA evaluation involvement, calibration experience, and any RLHF or LLM evaluation projects you have completed.

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