Senior Data Annotation Jobs in Denver

Senior data annotation jobs in Denver at Rex.zone focus on building high-quality training data for AI systems, including LLM training pipelines, RLHF (Reinforcement Learning from Human Feedback), data labeling, QA evaluation, and prompt evaluation. You will apply annotation guidelines compliance, measure training data quality, and drive model performance improvement by labeling text, images, and structured data for NLP, computer vision annotation, and content safety labeling workflows. This remote full-time role supports AI labs, tech startups, and annotation vendors by ensuring consistent labels, clear edge-case handling, and reliable evaluation signals that improve large language model evaluation outcomes and downstream product safety.

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Job Heading: Senior Data Annotation Jobs in Denver

Title: Senior Data Annotation 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, 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 complex annotation and evaluation workstreams that create reliable training signals for modern AI systems. The work includes text and prompt evaluation for LLMs, RLHF preference labeling, structured QA evaluation, and multimodal labeling (e.g., computer vision annotation). You will interpret and refine annotation guidelines, resolve ambiguous edge cases, and collaborate with data operations and engineering partners to deliver consistent, audit-ready datasets that improve model performance improvement and support safe deployment.

What You Will Do

You will execute and review high-difficulty labeling tasks across NLP and multimodal projects. You will run QA evaluation audits, identify disagreement patterns, and propose guideline updates to increase annotation guidelines compliance. You will perform prompt evaluation and RLHF ranking, document rationales, and ensure calibration across annotators. You will support large language model evaluation by creating gold sets, measuring training data quality, and validating dataset integrity prior to release into LLM training pipelines.

Core Workflows You Will Support

You will contribute to RLHF pipelines (pairwise preference, ranking, and rubric-based evaluations), supervised fine-tuning data labeling, and content safety labeling for policy-aligned outputs. You will handle named entity recognition (NER) tasks, taxonomy-based classification, and extraction for NLP datasets. You will support computer vision annotation including bounding boxes, polygons, keypoints, and attribute tagging when projects require multimodal data.

Required Qualifications

Experience operating as a senior or lead annotator with strong quality ownership. Proven ability to follow and improve annotation guidelines compliance, including edge-case adjudication. Familiarity with RLHF and large language model evaluation concepts such as preference labels, rubric scoring, and prompt evaluation. Comfort working with production-quality QA evaluation processes, disagreement analysis, and dataset versioning.

Preferred Qualifications

Experience with NLP tasks such as named entity recognition, sentiment or intent classification, and structured extraction. Experience with computer vision annotation tools and specs. Background supporting content safety labeling and policy-based evaluations. Ability to communicate clearly with engineering and data operations partners about schema decisions, acceptance criteria, and training data quality metrics.

Tools and Data Practices

You will use labeling platforms and internal QA tooling to apply schemas consistently, track defects, and manage annotation batches. You will document decisions, maintain calibration notes, and ensure traceability across dataset revisions. You will apply careful handling of sensitive content, follow privacy and safety requirements, and contribute to repeatable evaluation workflows for LLM training pipelines.

Remote Work and Collaboration

This is a Remote, FULL_TIME role supporting Denver-aligned hiring intent while operating in distributed teams. You will collaborate asynchronously, participate in calibration sessions, and coordinate with project leads to hit quality and throughput targets. Remote work requires strong documentation habits, careful rubric interpretation, and consistent QA evaluation discipline.

Compensation

Salary range is 63360 to 126720 USD per YEAR, based on skills, scope, and project complexity. This posting reflects full-time remote hiring needs aligned to senior data annotation jobs in Denver.

How to Apply on Rex.zone

Explore this role on Rex.zone and submit your application with a brief summary of annotation experience, domains (NLP, computer vision annotation, content safety labeling), and any RLHF or prompt evaluation work. Include examples of QA evaluation responsibilities, guideline writing, or disagreement analysis that demonstrate training data quality ownership.

Frequently Asked Questions

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

    Yes. Remote Type is Remote, and the role is designed for distributed collaboration while matching Denver-focused recruiting intent.

  • Q: What kind of data will I annotate?

    You may label text for NLP tasks (including named entity recognition), perform RLHF preference ranking, conduct prompt evaluation for LLMs, run QA evaluation audits, and participate in computer vision annotation or content safety labeling depending on project needs.

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

    Senior means you will handle complex edge cases, maintain annotation guidelines compliance, lead calibration, perform quality reviews, and help improve training data quality that drives model performance improvement.

  • Q: Do I need machine learning engineering experience?

    No. This is an AI data operations role, but you should understand how labeled data and evaluation signals flow into LLM training pipelines and large language model evaluation.

  • Q: Is this role full-time and what is the pay range?

    Yes, Employment Type is FULL_TIME. The salary range is 63360 to 126720 USD per YEAR.

  • Q: What industries or employer types does this support?

    The work supports Technology teams and can serve AI labs, tech startups, BPOs, and annotation vendors that require reliable labeling and QA evaluation for production AI systems.

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