[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotation-jobs-dallas":3},{"Ques":4,"Slug":34,"Header":35,"job_category":63},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28,31],{"A":8,"Q":9},"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.","Is this a remote role even though it targets senior data annotation jobs in Dallas?",{"A":11,"Q":12},"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.","What does a senior data annotation specialist do day to day?",{"A":14,"Q":15},"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.","What AI domains are covered in this job?",{"A":17,"Q":18},"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.","What does RLHF mean in the context of data annotation?",{"A":20,"Q":21},"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.","What is prompt evaluation?",{"A":23,"Q":24},"This posting is for FULL_TIME employment. Rex.zone may host contract or freelance roles separately, but this role remains full-time and remote.","Is this contract or freelance?",{"A":26,"Q":27},"Experience Level is Mid-Senior. Candidates should be comfortable reviewing others’ work, handling ambiguity, and contributing to QA evaluation and guideline refinement.","What experience level is required?",{"A":29,"Q":30},"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.","What skills should I highlight to match this role?",{"A":32,"Q":33},"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.","How does this role contribute to model performance improvement?","senior-data-annotation-jobs-dallas",{"desc":36,"title":37,"content":38},"Senior data annotation professionals in Dallas help AI teams at Rex.zone build reliable training datasets for real-world AI\u002FML 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.","Senior Data Annotation Jobs in Dallas",[39,42,45,48,51,54,57,60],{"h2":40,"desc":41},"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.\nTitle: Senior Data Annotation Specialist (Dallas)\nDate: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: 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\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":43,"desc":44},"What You Will Do","Execute and review data labeling tasks for NLP, named entity recognition, and document understanding use cases.\nPerform RLHF evaluation and comparative ranking to improve alignment, helpfulness, and instruction-following behavior.\nConduct prompt evaluation and response grading using clear rubrics and calibrated scoring.\nRun QA evaluation checks to ensure training data quality, consistency, and low inter-annotator disagreement.\nCreate and refine annotation guidelines, edge-case policies, and decision trees to maintain annotation guidelines compliance.\nSupport computer vision annotation (bounding boxes, polygons, keypoints) when project scope requires multimodal labeling.\nLabel and review content safety labeling across policy categories (self-harm, hate, sexual content, violence) with high precision.\nDocument issues in datasets (noise, ambiguity, distribution shift) and propose fixes to improve downstream model performance.\nCollaborate with AI\u002FML engineers, data operations leads, and QA reviewers to improve throughput and quality metrics in LLM training pipelines.",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience in data annotation, data labeling, or AI\u002FML evaluation workflows.\nDemonstrated ability to follow and enforce detailed annotation guidelines compliance across ambiguous edge cases.\nExperience with training data quality processes such as sampling plans, adjudication, calibration, and error taxonomy.\nHands-on exposure to LLM evaluation, RLHF-style ranking, or prompt evaluation with rubric-based scoring.\nStrong written reasoning skills to justify labels, escalations, and QA decisions with clear evidence.\nComfort working in a Remote full-time environment with structured throughput and quality targets.",{"h2":49,"desc":50},"Preferred Qualifications","Experience supporting large language model evaluation for instruction following, factuality, safety, and style.\nBackground in named entity recognition, text classification, summarization evaluation, or retrieval-augmented tasks.\nComputer vision annotation familiarity (CV) for image\u002Fvideo labeling and quality review.\nContent safety labeling experience with policy interpretation and consistent enforcement.\nExperience improving operational quality systems (gold sets, calibration sessions, reviewer checklists, disagreement analysis).",{"h2":52,"desc":53},"Tools and Workflows You Will Use","Annotation platforms and labeling interfaces for text, image, and multimodal tasks.\nRubrics for QA evaluation, prompt evaluation, and RLHF comparative ranking.\nQuality operations methods: gold data, audits, adjudication, inter-annotator agreement, and error analysis.\nSecure handling practices for sensitive datasets and content safety labeling workflows.",{"h2":55,"desc":56},"How Success Is Measured","Training data quality improvements and reduced label error rates over time.\nHigh annotation guidelines compliance with consistent handling of edge cases.\nReliable QA evaluation results and actionable feedback that improves annotator calibration.\nEfficient throughput that maintains quality standards in LLM training pipelines.\nClear documentation that supports model performance improvement and dataset iteration.",{"h2":58,"desc":59},"Why Rex.zone","Rex.zone connects senior annotation talent with AI labs, tech startups, annotation vendors, and enterprise teams.\nProjects may include NLP, computer vision annotation, content safety labeling, and large language model evaluation.\nA Remote full-time structure designed for consistent delivery, clear metrics, and repeatable quality processes.\nA practical path to grow into reviewer, QA lead, or data operations leadership tracks within AI Data Operations.",{"h2":61,"desc":62},"Apply","Apply through Rex.zone with a resume highlighting data annotation, RLHF, QA evaluation, and prompt evaluation experience.\nInclude examples of guideline interpretation, edge-case handling, and training data quality improvements if available.\nQualified candidates may be asked to complete a short calibration-style evaluation relevant to LLM evaluation or data labeling.","AI Data Operations"]