Senior Data Annotation Jobs in Dallas

Senior data annotation professionals in Dallas help AI teams at Rex.zone build reliable training datasets for real-world AI/ML systems. In this full-time remote role, you will perform data labeling, RLHF evaluation, QA review, and prompt evaluation to improve large language model evaluation and model performance improvement. You will apply annotation guidelines compliance, measure training data quality, and support LLM training pipelines across NLP, named entity recognition, computer vision annotation, and content safety labeling. If you want senior data annotation jobs Dallas recruiters can source for high-impact projects, explore and apply on Rex.zone.

Job Overview — Senior Data Annotation (Dallas)

You will lead high-quality data annotation and evaluation workflows that directly impact model training outcomes. This position focuses on consistent labeling decisions, reviewer-level QA, and structured feedback loops (including RLHF and prompt evaluation) to support large language model evaluation and continuous model performance improvement. Although this role is aligned to Dallas-based recruiting needs, it is explicitly Remote and supports cross-functional teams building NLP, CV, and safety systems. Title: Senior Data Annotation Specialist (Dallas) 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: Senior data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, training data quality, annotation guidelines compliance, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

What You Will Do

Execute and review data labeling tasks for NLP, named entity recognition, and document understanding use cases. Perform RLHF evaluation and comparative ranking to improve alignment, helpfulness, and instruction-following behavior. Conduct prompt evaluation and response grading using clear rubrics and calibrated scoring. Run QA evaluation checks to ensure training data quality, consistency, and low inter-annotator disagreement. Create and refine annotation guidelines, edge-case policies, and decision trees to maintain annotation guidelines compliance. Support computer vision annotation (bounding boxes, polygons, keypoints) when project scope requires multimodal labeling. Label and review content safety labeling across policy categories (self-harm, hate, sexual content, violence) with high precision. Document issues in datasets (noise, ambiguity, distribution shift) and propose fixes to improve downstream model performance. Collaborate with AI/ML engineers, data operations leads, and QA reviewers to improve throughput and quality metrics in LLM training pipelines.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, or AI/ML evaluation workflows. Demonstrated ability to follow and enforce detailed annotation guidelines compliance across ambiguous edge cases. Experience with training data quality processes such as sampling plans, adjudication, calibration, and error taxonomy. Hands-on exposure to LLM evaluation, RLHF-style ranking, or prompt evaluation with rubric-based scoring. Strong written reasoning skills to justify labels, escalations, and QA decisions with clear evidence. Comfort working in a Remote full-time environment with structured throughput and quality targets.

Preferred Qualifications

Experience supporting large language model evaluation for instruction following, factuality, safety, and style. Background in named entity recognition, text classification, summarization evaluation, or retrieval-augmented tasks. Computer vision annotation familiarity (CV) for image/video labeling and quality review. Content safety labeling experience with policy interpretation and consistent enforcement. Experience improving operational quality systems (gold sets, calibration sessions, reviewer checklists, disagreement analysis).

Tools and Workflows You Will Use

Annotation platforms and labeling interfaces for text, image, and multimodal tasks. Rubrics for QA evaluation, prompt evaluation, and RLHF comparative ranking. Quality operations methods: gold data, audits, adjudication, inter-annotator agreement, and error analysis. Secure handling practices for sensitive datasets and content safety labeling workflows.

How Success Is Measured

Training data quality improvements and reduced label error rates over time. High annotation guidelines compliance with consistent handling of edge cases. Reliable QA evaluation results and actionable feedback that improves annotator calibration. Efficient throughput that maintains quality standards in LLM training pipelines. Clear documentation that supports model performance improvement and dataset iteration.

Why Rex.zone

Rex.zone connects senior annotation talent with AI labs, tech startups, annotation vendors, and enterprise teams. Projects may include NLP, computer vision annotation, content safety labeling, and large language model evaluation. A Remote full-time structure designed for consistent delivery, clear metrics, and repeatable quality processes. A practical path to grow into reviewer, QA lead, or data operations leadership tracks within AI Data Operations.

Apply

Apply through Rex.zone with a resume highlighting data annotation, RLHF, QA evaluation, and prompt evaluation experience. Include examples of guideline interpretation, edge-case handling, and training data quality improvements if available. Qualified candidates may be asked to complete a short calibration-style evaluation relevant to LLM evaluation or data labeling.

Frequently Asked Questions

Q: Is this a remote role even though it targets senior data annotation jobs in Dallas?
Yes. The Remote Type is Remote and the role is designed for full-time remote work while supporting Dallas-area recruiting needs and US-based projects.
Q: What does a senior data annotation specialist do day to day?
Senior annotators label and review training data, run QA evaluation, handle edge cases, enforce annotation guidelines compliance, and contribute to RLHF and prompt evaluation to improve large language model evaluation.
Q: What AI domains are covered in this job?
The role can cover NLP (including named entity recognition), computer vision annotation, content safety labeling, and LLM training pipelines with emphasis on training data quality and model performance improvement.
Q: What does RLHF mean in the context of data annotation?
RLHF (Reinforcement Learning from Human Feedback) uses human judgments—often pairwise rankings or rubric-based scores—to guide model behavior. In this role, you will perform RLHF evaluation tasks to help align outputs with quality and safety expectations.
Q: What is prompt evaluation?
Prompt evaluation is grading model responses to specific prompts using rubrics for helpfulness, correctness, policy compliance, and style. It is a core part of large language model evaluation and supports model performance improvement.
Q: Is this contract or freelance?
This posting is for FULL_TIME employment. Rex.zone may host contract or freelance roles separately, but this role remains full-time and remote.
Q: What experience level is required?
Experience Level is Mid-Senior. Candidates should be comfortable reviewing others’ work, handling ambiguity, and contributing to QA evaluation and guideline refinement.
Q: What skills should I highlight to match this role?
Highlight senior data annotation, data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines.
Q: How does this role contribute to model performance improvement?
High-quality labels, consistent rubrics, and disciplined QA evaluation reduce noise in datasets, improve supervision signals, and strengthen large language model evaluation—leading to measurable model performance improvement.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

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