Remote Data Labeling Jobs in Raleigh

Remote data labeling jobs in Raleigh at Rex.zone focus on creating high-quality training data for AI/ML systems across NLP and computer vision. You will label text, images, audio, and video, follow annotation guidelines compliance, and perform QA evaluation that improves model performance and training data quality for large language model evaluation and LLM training pipelines. Typical work includes RLHF preference ranking, prompt evaluation, named entity recognition, content safety labeling, and computer vision annotation such as bounding boxes and segmentation. Explore full-time remote opportunities and support AI labs, tech startups, and annotation vendors with reliable, measurable data labeling output.

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Remote Data Labeling Jobs in Raleigh — LinkedIn Job Metadata

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

About the Role

As a Remote Data Labeling Specialist based in Raleigh, you will produce and verify labeled datasets used to train and evaluate AI models deployed in real products. Your work supports LLM training pipelines and evaluation loops, including RLHF ranking, prompt evaluation, and quality assurance checks that reduce noise and improve model performance improvement. You will apply consistent annotation guidelines, document edge cases, and collaborate asynchronously with project leads to meet throughput and accuracy targets on Rex.zone.

What You Will Work On

You will label and review multi-modal data for AI training and evaluation. Common task types include text classification, named entity recognition, sentiment and intent labeling, content safety labeling, and LLM response quality evaluation. For computer vision annotation, you may create bounding boxes, polygons, segmentation masks, keypoints, and attributes. You will also perform QA evaluation through sampling, adjudication, disagreement analysis, and error taxonomy to strengthen training data quality.

Key Responsibilities

Deliver accurate labels aligned to annotation guidelines and project rubrics. Perform RLHF preference ranking and prompt evaluation to support large language model evaluation. Execute QA evaluation workflows: spot checks, gold set validation, inter-annotator agreement review, and corrective feedback loops. Identify ambiguous cases and propose guideline updates with clear examples. Maintain productivity targets while preserving precision and consistency across batches. Protect data privacy and follow content policies, especially for content safety labeling tasks.

Required Qualifications

2+ years in data labeling, data annotation, QA evaluation, or related AI data operations. Strong attention to detail with proven annotation guidelines compliance. Experience with LLM evaluation, RLHF-style ranking, or prompt evaluation is preferred. Familiarity with NLP tasks (NER, classification) and/or computer vision annotation (bounding boxes, segmentation). Ability to write clear rationales, track edge cases, and work independently in a remote environment.

Tools and Workflow

You will use labeling platforms and internal tooling to complete tasks, review peer work, and report issues. Work is organized into batches with defined acceptance criteria, gold samples, and measurable quality thresholds. You will contribute to model performance improvement by flagging systematic errors, improving guideline clarity, and maintaining consistent labeling across datasets used in LLM training pipelines.

Why Rex.zone

Rex.zone connects remote professionals to structured AI data work across NLP, computer vision, and content safety. You will gain exposure to real-world annotation workflows used by AI labs, tech startups, BPOs, and annotation vendors, with clear quality metrics and feedback cycles. This role is full-time remote and designed for mid-senior contributors who can balance speed with rigorous training data quality standards.

How to Apply

Apply through Rex.zone with a resume highlighting data labeling, QA evaluation, and any RLHF, LLM evaluation, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling experience. Include examples of guideline-driven work, quality metrics you’ve met, and the domains you’ve labeled (text, images, audio, video).

Frequently Asked Questions

  • Q: Are these remote data labeling jobs open to candidates in Raleigh?

    Yes. These are remote data labeling jobs aligned to Raleigh-based job seekers, but work is performed remotely within the US and remains marked Remote.

  • Q: What does a remote data labeling specialist do day to day?

    Typical work includes labeling and reviewing text, images, audio, or video; following annotation guidelines compliance; performing QA evaluation; and completing LLM evaluation tasks such as RLHF ranking and prompt evaluation to improve training data quality.

  • Q: What domains are covered (NLP, computer vision, content safety)?

    Projects commonly span NLP (named entity recognition, classification), computer vision annotation (bounding boxes, segmentation), and content safety labeling, depending on client needs and model training pipelines.

  • Q: Is this full-time or contract/freelance?

    This posting is for FULL_TIME remote work. Rex.zone may also list contract and freelance roles separately, but this role’s employment type is FULL_TIME.

  • Q: What experience level is expected?

    The role is Mid-Senior. You should be comfortable with complex annotation rubrics, QA evaluation practices, and consistent decision-making across edge cases.

  • Q: How is quality measured in data labeling?

    Quality is measured through acceptance criteria such as gold set accuracy, inter-annotator agreement, sampling audits, disagreement resolution, and documented error taxonomy that supports model performance improvement.

  • Q: What skills are most important for ranking and evaluation tasks like RLHF?

    Strong rubric adherence, consistent reasoning, prompt evaluation judgment, ability to compare outputs for helpfulness and safety, and careful documentation are key for RLHF and large language model evaluation workflows.

  • Q: Who hires for remote data labeling work on Rex.zone?

    Roles may support AI labs, tech startups, BPOs, and annotation vendors that require scalable training data quality for NLP, computer vision, and content safety systems.

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