Remote Data Labeling Jobs in Austin

Remote Data Labeling Jobs in Austin at Rex.zone focus on producing high-quality training data for AI/ML systems through data labeling, RLHF preference ranking, QA evaluation, and prompt evaluation. You will follow annotation guidelines compliance, improve training data quality, and support large language model evaluation and computer vision annotation workflows that drive model performance improvement. This role connects directly to LLM training pipelines, content safety labeling, and named entity recognition tasks used by AI labs, tech startups, and annotation vendors hiring through Rex.zone. Apply to help build reliable datasets for NLP, CV, and multimodal models.

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Remote Data Labeling Jobs in Austin

Title: Remote Data Labeling Specialist (Austin) 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, Training data quality, Named entity recognition, Computer vision annotation, Content safety labeling, Taxonomy and ontology Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will deliver production-grade annotations for NLP, computer vision, and LLM training workflows. Your work will include labeling and verification, RLHF preference comparisons, prompt-response evaluation, and content safety labeling. You will apply consistent taxonomies, follow detailed rubrics, document edge cases, and collaborate with operations and engineering stakeholders to reduce ambiguity and improve throughput without sacrificing quality.

What You Will Do

["Label and categorize text, images, and multimodal content using platform tooling and strict annotation guidelines","Perform RLHF tasks such as preference ranking, pairwise comparisons, and rubric-based scoring to improve large language model alignment","Execute QA evaluation: audit samples, identify systematic errors, and provide feedback to raise training data quality","Run prompt evaluation and response quality checks for helpfulness, harmlessness, and factuality within defined policies","Apply named entity recognition and span labeling for structured extraction tasks (entities, relations, attributes)","Support computer vision annotation such as bounding boxes, polygons, segmentation masks, keypoints, and image classification when required","Document edge cases, update labeling notes, and propose guideline clarifications to improve inter-annotator agreement","Work cross-functionally with project leads to monitor throughput, coverage, and model performance improvement signals"]

Required Qualifications

["Mid-Senior experience delivering data labeling or data annotation in a production environment","Proven ability to follow annotation guidelines compliance and maintain consistency across large batches","Experience with QA evaluation workflows (sampling, error taxonomy, root-cause analysis)","Familiarity with RLHF concepts and large language model evaluation (rubrics, preference data, safety checks)","Strong written communication for documenting rationales and edge cases","Comfort working with web-based annotation tools and spreadsheet-like review queues"]

Preferred Qualifications

["Experience with NLP tasks such as named entity recognition, intent classification, and text normalization","Experience with computer vision annotation (bounding boxes, polygons, segmentation)","Background in content safety labeling or policy-based moderation datasets","Understanding of dataset versioning, gold sets, and inter-annotator agreement metrics","Exposure to prompt evaluation and LLM training pipelines in applied settings"]

Quality Standards You Will Be Measured On

["Training data quality: accuracy, consistency, and completeness aligned to rubrics","Annotation guidelines compliance: adherence to definitions, edge-case handling, and documentation","Model performance improvement support: ability to surface ambiguous cases that degrade evaluation signal","QA evaluation impact: reduced defect rates and improved reviewer agreement","Operational reliability: throughput with sustained quality in full-time remote work"]

Work Context

["Remote: this role is Remote and can be performed from Austin, Texas or anywhere in the US","Employment Type: FULL_TIME","Team environment: collaboration with AI data operations, project management, and engineering stakeholders","Domains: NLP, computer vision, content safety, LLM training pipelines","Employer mix: AI labs, tech startups, BPOs, and annotation vendors sourcing work via Rex.zone"]

How to Apply on Rex.zone

["Visit Rex.zone and search: remote data labeling jobs Austin","Submit your profile with relevant annotation, QA evaluation, and RLHF experience","Complete any required qualification tasks (sample labeling, prompt evaluation, or rubric scoring)","Start with project onboarding and guideline calibration to align on training data quality expectations"]

Frequently Asked Questions

  • Q: Are these remote data labeling jobs in Austin truly remote?

    Yes. Remote Type is Remote and the work is performed remotely, with Austin used as the targeted search location and candidate market.

  • Q: What kind of data labeling tasks are included?

    Typical tasks include text classification, named entity recognition, prompt evaluation, RLHF preference ranking, QA evaluation audits, content safety labeling, and computer vision annotation such as bounding boxes or segmentation when needed.

  • Q: What does RLHF work look like in this role?

    You will compare model outputs, rank responses, apply rubric-based scoring, and document rationales so preference data can improve alignment and model performance improvement in LLM training pipelines.

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

    This posting is FULL_TIME. Rex.zone may also list contract or freelance roles, but this specific role remains full-time per the job metadata.

  • Q: What experience level is expected?

    Experience Level is Mid-Senior. You should be comfortable with complex guidelines, QA evaluation processes, and consistent decision-making across large annotation volumes.

  • Q: What tools will I use for annotation and evaluation?

    You will primarily use web-based labeling interfaces, review queues, and guideline documentation systems. Tooling varies by project but is designed for auditable, rubric-driven labeling and QA evaluation.

  • Q: How is quality measured?

    Quality is measured through training data quality metrics, guideline compliance, reviewer agreement, QA evaluation defect rates, and the clarity of your edge-case documentation.

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

    The work supports Technology organizations including AI labs, tech startups, BPOs, and annotation vendors that require reliable datasets for NLP, computer vision, and content safety systems.

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