Remote Data Labeling Jobs in the United States

Remote data labeling jobs in the United States focus on creating high-quality training data for AI systems, including large language model evaluation, RLHF feedback, and annotation guidelines compliance across NLP and computer vision workflows. On Rex.zone, these roles support end-to-end LLM training pipelines by labeling text, images, and conversations, running QA evaluation checks, and performing prompt evaluation to drive model performance improvement. You will follow detailed taxonomies, apply content safety labeling policies, and measure training data quality with systematic reviews. If you are seeking full-time remote work with clear annotation standards, measurable outputs, and real impact on production AI, explore and apply on Rex.zone.

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Remote Data Labeling Specialist (United States)

Title: Remote Data Labeling Specialist (United States) 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, LLM evaluation, Prompt evaluation, QA evaluation, Annotation guidelines compliance, Training data quality, Named entity recognition, Computer vision annotation, Content safety labeling, Taxonomy development Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will label and evaluate multi-modal AI training data (text, image, and conversational turns) to improve model performance in production systems. Your work will include RLHF-style ranking and preference labeling, prompt evaluation for helpfulness and factuality, and QA evaluation to ensure annotation guidelines compliance. You will partner with data operations and engineering stakeholders to refine taxonomies, resolve edge cases, and maintain consistent training data quality across batches for NLP and computer vision annotation projects.

What You Will Do

Core responsibilities include: (1) Execute data labeling and data annotation tasks for LLM training pipelines, including classification, extraction, and ranking; (2) Perform RLHF workflows such as preference comparisons, rationale tagging, and rubric-based scoring; (3) Conduct large language model evaluation and prompt evaluation for safety, relevance, grounding, and instruction-following; (4) Apply named entity recognition and span labeling with strict annotation guidelines compliance; (5) Support computer vision annotation such as bounding boxes, polygons, keypoints, and image categorization when projects require; (6) Run QA evaluation, inter-annotator agreement checks, and targeted audits to ensure training data quality; (7) Perform content safety labeling and policy-based moderation tagging for sensitive categories; (8) Document edge cases, propose taxonomy updates, and escalate ambiguous items with clear evidence.

Required Qualifications

You should have experience in production data labeling or related QA evaluation workflows, and be comfortable working with detailed rubrics and annotation guidelines compliance requirements. You can read and apply policy language precisely, maintain high throughput without sacrificing training data quality, and communicate clearly in asynchronous remote settings. Familiarity with NLP concepts (intent, entities, sentiment), named entity recognition, and evaluation of LLM outputs is strongly preferred.

Preferred Qualifications

Preferred: prior RLHF experience, prompt evaluation for LLMs, content safety labeling, and exposure to computer vision annotation tasks. Experience with audit processes, error taxonomies, adjudication workflows, and measuring model performance improvement through better training data is a plus. Comfort working with annotation tools, structured labeling UIs, and basic spreadsheet/SQL-style filtering is helpful.

How Work Is Measured

Success is measured via training data quality metrics (accuracy, consistency, rubric adherence), annotation guidelines compliance, QA evaluation pass rates, and the ability to resolve edge cases with minimal rework. You will also be evaluated on clarity of documentation, reliability in meeting SLAs, and contribution to model performance improvement through actionable feedback to the labeling and evaluation process.

Remote Work Details

This is a full-time Remote role in the United States. Work is primarily asynchronous, with scheduled syncs as needed for calibration, rubric updates, and QA evaluation reviews. You must maintain secure handling of datasets and follow project confidentiality requirements.

Apply on Rex.zone

To apply, use Rex.zone to find the matching remote data labeling job in the United States, submit your profile, and complete any required evaluation tasks. Keep your experience focused on data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to align with current project needs.

Frequently Asked Questions

  • Q: What are remote data labeling jobs in the United States?

    Remote data labeling jobs in the United States involve creating and validating labeled datasets used to train and evaluate AI models. Typical work includes data annotation for NLP and computer vision annotation, RLHF preference labeling, prompt evaluation, QA evaluation, and content safety labeling to improve training data quality and downstream model performance.

  • Q: Is this role fully Remote?

    Yes. The role is explicitly marked Remote and is designed for full-time remote work within the US, with process-driven workflows, calibration sessions, and QA evaluation checkpoints handled online.

  • Q: What kinds of tasks will I label or evaluate?

    You may label text for named entity recognition and classification, rank model responses in RLHF workflows, perform large language model evaluation and prompt evaluation, and annotate images for computer vision annotation when required. You will also complete QA evaluation audits to ensure annotation guidelines compliance.

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

    Highlight data labeling, data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality practices, named entity recognition, computer vision annotation fundamentals, content safety labeling, and experience working with taxonomies and rubrics.

  • Q: Are there other job modifiers supported on Rex.zone for similar work?

    Yes. Depending on project needs, Rex.zone roles may include remote, contract, freelance, full-time, entry-level, or senior opportunities across NLP, computer vision, content safety, and LLM training pipelines for AI labs, tech startups, BPOs, and annotation vendors.

  • Q: How does QA evaluation fit into data labeling workflows?

    QA evaluation ensures training data quality by checking rubric adherence, annotation guidelines compliance, consistency across annotators, and correctness on sampled audits. Strong QA processes reduce noise in datasets and support model performance improvement during training and evaluation.

  • Q: What is RLHF and why is it mentioned in data labeling jobs?

    RLHF (Reinforcement Learning from Human Feedback) uses human preference data to train models to produce better responses. In practice, it includes pairwise comparisons, rankings, and rubric-based scoring that function as specialized data labeling for large language model evaluation and alignment.

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