[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-atlanta":3},{"Ques":4,"Slug":31,"Header":32,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. The role is explicitly marked Remote while targeting candidates in the Atlanta market for “senior data labeling jobs Atlanta” search intent.","Are these senior data labeling jobs in Atlanta remote?",{"A":11,"Q":12},"This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance data labeling roles separately, but this page is for full-time senior hiring.","Is this a full-time role or contract\u002Ffreelance?",{"A":14,"Q":15},"Senior scope typically includes complex annotation, consistent guideline application, ownership of training data quality, QA evaluation oversight, calibration support, and contributions to rubric design for large language model evaluation.","What does “senior” mean for a data labeling role?",{"A":17,"Q":18},"Depending on project needs, tasks can include NLP labeling (classification, NER), RLHF preference ranking and prompt evaluation for LLMs, computer vision annotation (bounding boxes\u002Fsegmentation), and content safety labeling using policy-driven taxonomies.","What kinds of tasks are included (NLP, computer vision, content safety)?",{"A":20,"Q":21},"RLHF experience is helpful but not always required. Strong data labeling fundamentals, annotation guidelines compliance, and QA evaluation discipline are essential, and you can ramp into RLHF workflows through calibrated rubrics and feedback.","Do I need prior RLHF experience?",{"A":23,"Q":24},"Quality is measured through QA evaluation processes such as sampling audits, inter-annotator agreement, error categorization, guideline adherence, and consistency checks tied to training data quality and model performance improvement goals.","How is quality measured in this role?",{"A":26,"Q":27},"Include skills aligned to the posting intent: data labeling, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, and training data quality.","What skills should I include to match this job?",{"A":29,"Q":30},"The work supports AI labs, tech startups, BPOs, and annotation vendors by delivering reliable labeled data and evaluation signals for production AI\u002FML and LLM training pipelines.","Which employer types does this work support?","senior-data-labeling-jobs-atlanta",{"desc":33,"title":34,"content":35},"Rex.zone is hiring for senior data labeling jobs in Atlanta (Remote, Full-Time). This role is a search-recognizable data labeling and AI\u002FML evaluation position focused on training data quality, annotation guidelines compliance, and large language model evaluation. You will contribute to LLM training pipelines through RLHF, prompt evaluation, QA evaluation, and content safety labeling across NLP and computer vision annotation tasks. If you have hands-on experience improving model performance with high-precision labeled datasets, apply to Rex.zone to support AI labs, tech startups, and annotation vendors with scalable, reliable data operations.","Senior Data Labeling Jobs in Atlanta",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Overview","keyword: Senior Data Labeling Jobs in Atlanta | job_title: Senior Data Labeling Specialist (Atlanta)\nDate: 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, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR\nYou will lead and execute high-accuracy data labeling and evaluation work that directly supports AI\u002FML training workflows, including LLM training pipelines, RLHF preference ranking, and multi-stage QA evaluation. This position emphasizes end-to-end training data quality: interpreting annotation guidelines, resolving edge cases, calibrating annotators, and producing consistent labels for downstream model performance improvement.",{"h2":40,"desc":41},"What You Will Work On","You will deliver labeled datasets and evaluation signals used to train, fine-tune, and validate models across NLP, computer vision, and content safety domains. Work includes prompt evaluation, response ranking for RLHF, named entity recognition (NER) and entity linking, taxonomy-driven content safety labeling, and image\u002Fvideo annotation. You will partner with data ops and engineering stakeholders to translate model requirements into annotation guidelines compliance and measurable quality targets.",{"h2":43,"desc":44},"Core Responsibilities","You will own consistent labeling outputs and quality standards across projects, focusing on training data quality and evaluation rigor. Responsibilities include: executing complex annotation tasks; performing QA evaluation (spot checks, audits, inter-annotator agreement); writing and refining annotation guidelines; handling ambiguous cases with documented rationales; supporting rubric design for large language model evaluation; and providing feedback loops that drive model performance improvement.",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience in data labeling, data annotation, or AI\u002FML evaluation with demonstrated accuracy and throughput. Strong understanding of annotation guidelines compliance, error taxonomy, and quality measurement (e.g., agreement scoring, sampling strategies). Familiarity with NLP concepts (NER, intent\u002Fslot labeling, classification), LLM evaluation (rubrics, prompt evaluation, preference ranking), and\u002For computer vision annotation (bounding boxes, polygons, segmentation). Comfort working in remote, metrics-driven production environments.",{"h2":49,"desc":50},"Preferred Qualifications","Experience with RLHF workflows (pairwise ranking, preference data, reasoning trace evaluation where applicable), content safety labeling policies, multilingual evaluation, and prompt-based testing for LLM behavior. Prior work with AI labs, tech startups, BPOs, or annotation vendors. Ability to mentor peers, drive calibration sessions, and improve SOPs for scalable labeling operations.",{"h2":52,"desc":53},"Tools, Data Types, and Workflow","You will work with structured and unstructured datasets including text, conversation logs, and image\u002Fvideo samples. Typical workflows include task ingestion, guideline review, calibration, annotation, QA evaluation, disagreement resolution, and delivery with clear documentation. You will apply consistent rubrics for large language model evaluation and maintain traceability for decisions that affect training data quality.",{"h2":55,"desc":56},"Compensation and Employment Details","keyword: Senior Data Labeling Jobs in Atlanta | job_title: Senior Data Labeling Specialist (Atlanta)\nDate: 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, RLHF, LLM evaluation, QA evaluation, prompt evaluation, annotation guidelines, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR\nThis is a Remote, Full-Time role aligned to senior data labeling work supporting production AI\u002FML pipelines. Compensation range is USD 63360 to USD 126720 per year, depending on scope, domain complexity (NLP, computer vision, content safety), and demonstrated evaluation\u002FQA leadership.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your role-relevant background (data labeling, QA evaluation, RLHF or prompt evaluation exposure) and examples of guideline-driven work. Highlight experience improving training data quality, handling edge cases, and delivering consistent outputs that support model performance improvement. Candidates may be asked to complete a short calibration-style assessment aligned with large language model evaluation or annotation guidelines compliance.","AI Data Operations"]