Remote Data Labeling Jobs in Phoenix

Remote data labeling jobs in Phoenix are mid-senior, full-time roles focused on creating and validating training data for AI systems on Rex.zone. You will label text, images, video, and audio; follow annotation guidelines compliance; run QA evaluation; and support RLHF and prompt evaluation to improve large language model evaluation and model performance improvement. This work directly impacts training data quality, content safety labeling, named entity recognition, and computer vision annotation used by AI labs, tech startups, and annotation vendors building production LLM training pipelines.

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Remote Data Labeling Jobs in Phoenix

Title: Remote Data Labeling Specialist (Phoenix) 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, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, content safety labeling, computer vision annotation, large language model evaluation, LLM training pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will produce high-quality labeled datasets that power AI/ML model training and evaluation. Day-to-day work includes labeling and reviewing tasks across NLP and computer vision, applying consistent taxonomy and edge-case handling, and documenting decisions to maintain training data quality. You will support RLHF by ranking model responses, performing prompt evaluation, and auditing outputs for content safety labeling. You will also run QA evaluation workflows (spot checks, inter-annotator agreement, guideline drift checks) to drive model performance improvement in large language model evaluation pipelines.

What You Will Do

You will execute labeling tasks for text, image, video, and audio; apply annotation guidelines compliance; and flag ambiguous cases for clarification. You will perform named entity recognition, sentiment/intent tagging, and instruction-following judgments used in LLM training pipelines. You will complete RLHF tasks such as preference ranking, harmlessness/helpfulness checks, and rubric-based scoring for prompt evaluation. You will conduct QA evaluation including sampling plans, error categorization, rework coordination, and final dataset sign-off to protect training data quality.

Required Qualifications

Mid-senior experience in data labeling or data annotation programs with measurable quality outcomes. Strong ability to interpret guidelines, maintain consistency, and write clear adjudication notes for edge cases. Familiarity with QA evaluation concepts such as precision/recall style thinking, inter-annotator agreement, and error taxonomy. Working knowledge of NLP and computer vision annotation, including named entity recognition and bounding box/polygon concepts. Comfort contributing to large language model evaluation, RLHF, and prompt evaluation tasks with attention to content safety labeling requirements.

Preferred Qualifications

Experience with multi-stage review pipelines, gold sets, and calibration sessions to improve training data quality. Background supporting AI labs, tech startups, BPOs, or annotation vendors delivering datasets at scale. Familiarity with policy-driven content safety labeling and sensitive topic handling. Exposure to dataset versioning, annotation tool configuration, and lightweight analytics for QA evaluation. Understanding of how labeled data affects model performance improvement in LLM training pipelines.

Work Model, Team, and Tooling

This is a Remote, FULL_TIME role aligned to Phoenix candidates and time zones as needed. You will work with data operations, QA reviewers, and engineering stakeholders to align guidelines with model goals. Tooling may include web-based annotation platforms, rubrics for RLHF and prompt evaluation, and QA dashboards for tracking training data quality. You will collaborate asynchronously and document decisions to keep annotation guidelines compliance consistent across projects.

How to Apply on Rex.zone

Apply through Rex.zone by selecting the Remote Data Labeling Specialist (Phoenix) listing and submitting your resume and any relevant work samples (redacted). Emphasize experience with data labeling, QA evaluation, RLHF, prompt evaluation, and large language model evaluation. Candidates may complete a skills assessment covering annotation guidelines compliance, edge-case reasoning, and training data quality review.

Frequently Asked Questions

  • Q: What are remote data labeling jobs in Phoenix?

    They are remote roles where you create and validate labeled datasets used to train and evaluate AI models. Work commonly includes data annotation for NLP and computer vision, named entity recognition, content safety labeling, and QA evaluation to maintain training data quality for LLM training pipelines.

  • Q: Is this role fully remote and full-time?

    Yes. Remote Type is Remote and Employment Type is FULL_TIME.

  • Q: What kinds of tasks will I do in RLHF and prompt evaluation?

    You may rank model responses, score outputs using rubrics, compare alternatives for preference modeling, and evaluate instruction-following and safety behaviors. These tasks support large language model evaluation and model performance improvement.

  • Q: What skills are most important for success?

    Strong data labeling and data annotation fundamentals, annotation guidelines compliance, training data quality mindset, QA evaluation practices, and familiarity with named entity recognition, content safety labeling, computer vision annotation, RLHF, and prompt evaluation.

  • Q: What industries and employers use this work?

    AI labs, tech startups, BPOs, and annotation vendors use labeled data to build and evaluate models across NLP, computer vision, and content safety domains.

  • Q: What is the salary range for this posting?

    Salary Min is 63360 USD and Salary Max is 126720 USD, Pay Period is YEAR.

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