Senior Data Labeling Jobs in Nashville

Senior data labeling jobs in Nashville at Rex.zone focus on building high-quality training datasets for AI/ML systems by combining data labeling, RLHF evaluation, and QA review. You will apply annotation guidelines compliance across NLP and computer vision tasks, support large language model evaluation through prompt evaluation and preference ranking, and drive training data quality that supports model performance improvement. This remote, full-time role connects your labeling expertise to real-world LLM training pipelines, content safety labeling, and named entity recognition workflows used by AI labs, tech startups, and annotation vendors hiring through Rex.zone.

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About the Role

Own end-to-end data labeling and evaluation workflows for AI training datasets used in NLP, computer vision, and content safety. You will perform advanced annotation, lead QA evaluation, and execute RLHF-style preference ranking and prompt evaluation to improve large language model behavior. The work emphasizes training data quality, consistent guideline interpretation, and clear documentation that enables model performance improvement across iterative training cycles.

What You Will Do

Label and review complex datasets across text, image, and multimodal tasks, maintaining high inter-annotator agreement and measurable training data quality. Run QA evaluation programs: sampling plans, error taxonomy, root-cause analysis, and corrective actions tied to annotation guidelines compliance. Perform RLHF evaluation workflows such as pairwise preference ranking, rubric-based scoring, and prompt evaluation for instruction-following and safety behavior. Execute NLP labeling tasks including named entity recognition, classification, summarization evaluation, and groundedness/faithfulness checks. Execute computer vision annotation such as bounding boxes, polygons, keypoints, and attribute tagging; validate edge cases and ambiguous frames. Support content safety labeling for policy categories (e.g., harassment, self-harm, hate) with consistent escalation and audit trails. Collaborate with engineering and ML teams to translate model failure modes into updated guidelines, new label schemas, and targeted re-annotation. Document decisions, dataset specs, and QA findings so downstream users can reproduce results and trust dataset lineage.

Required Qualifications

3+ years in data labeling, data annotation, or AI/ML evaluation with demonstrated ownership of QA evaluation and reviewer workflows. Hands-on experience with large language model evaluation, RLHF evaluation, or structured prompt evaluation using rubrics and preference judgments. Strong understanding of annotation guidelines compliance, error analysis, and how training data quality impacts model performance improvement. Experience with NLP tasks such as named entity recognition and text classification, plus familiarity with common dataset formats and tooling. Experience with computer vision annotation concepts (bounding boxes, polygons, segmentation) and common ambiguity resolution patterns. Ability to write clear guidelines, make consistent labeling decisions, and communicate edge cases to cross-functional stakeholders.

Preferred Qualifications

Experience evaluating safety, policy, and content moderation tasks through content safety labeling and adversarial prompt testing. Background supporting AI labs, tech startups, BPOs, or annotation vendors with multi-client dataset requirements and SLA-driven QA. Comfort with rapid iteration cycles: guideline updates, rework planning, and measurement of precision/recall-style quality metrics. Familiarity with dataset auditing concepts such as bias checks, coverage analysis, and distribution drift in training corpora.

Tools and Workflows You Will Use

Annotation platforms and review queues for production labeling and QA evaluation. Rubric-based evaluation templates for LLM evaluation, prompt evaluation, and RLHF preference ranking. Structured sampling, audits, and issue-tracking for annotation guidelines compliance and continuous quality improvement. Dataset documentation practices that support lineage, reproducibility, and handoff to engineering pipelines.

Remote Work and Location Notes (Nashville)

This role is Remote and open to candidates based in the US, including Nashville, TN. You may be asked to align with specific team collaboration hours for reviews, calibrations, and QA sign-off meetings. Remote roles remain fully remote; no relocation is required.

Employment Type and Modifiers

Employment Type: FULL_TIME Remote: Remote Seniority: Mid-Senior (senior-scope responsibilities within data labeling and evaluation) Related modifiers covered: remote, full-time, contract, freelance, entry-level, senior (contract/freelance/entry-level may exist on Rex.zone, but this posting is full-time and remote)

How to Apply on Rex.zone

Visit Rex.zone and search: "Senior Data Labeling Jobs in Nashville" to find the current application flow. Prepare examples of QA evaluation work, guideline writing, and any RLHF evaluation or prompt evaluation experience. Submit availability, relevant task domains (NLP, computer vision, content safety), and tooling familiarity for faster matching.

Frequently Asked Questions

Q: What does a senior data labeling specialist do in AI/ML training?
A senior data labeling specialist produces and validates training data used by machine learning systems. The role typically includes complex annotation, reviewer-level QA evaluation, guideline ownership, and feedback loops that connect label quality to model performance improvement in LLM training pipelines, NLP datasets, and computer vision annotation.
Q: Is this role remote and full-time?
Yes. The posting is Remote and FULL_TIME, aligned to US-based candidates, including Nashville, TN.
Q: How does RLHF relate to data labeling?
RLHF (Reinforcement Learning from Human Feedback) relies on human judgments such as preference ranking, rubric scoring, and prompt evaluation. These judgments function as specialized labels that teach large language models to follow instructions, improve helpfulness, and reduce unsafe outputs.
Q: What task domains are covered in these senior data labeling jobs?
Common domains include NLP labeling (named entity recognition, classification, summarization evaluation), computer vision annotation (bounding boxes, polygons, segmentation), content safety labeling, and LLM evaluation tasks like prompt evaluation and preference ranking.
Q: What skills matter most for senior-level performance?
Training data quality ownership, annotation guidelines compliance, strong QA evaluation practices, consistent decision-making on edge cases, and the ability to translate model failure modes into guideline and schema improvements. Experience with LLM evaluation and RLHF evaluation is especially valuable.
Q: What types of employers hire through Rex.zone for this work?
Teams may include AI labs, technology companies, tech startups, BPOs, and annotation vendors supporting production-scale LLM training pipelines, NLP systems, and computer vision models.

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