[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-united-states":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 and is intended for candidates located in the US.","Are these senior data labeling jobs remote in the United States?",{"A":11,"Q":12},"Yes. The employment type is FULL_TIME and the posting is written for full-time delivery expectations.","Is this a full-time role?",{"A":14,"Q":15},"They produce and review high-quality labeled data, run RLHF preference rankings, perform prompt evaluation, and conduct QA evaluation to improve training data quality and model performance improvement.","What does a senior data labeling specialist do in LLM training pipelines?",{"A":17,"Q":18},"Work may include NLP tasks such as named entity recognition, computer vision annotation tasks such as segmentation and bounding boxes, and content safety labeling aligned with policy and risk categories.","What domains are covered (NLP, computer vision, content safety)?",{"A":20,"Q":21},"It is strongly preferred. If you have experience with preference ranking, rubric-based evaluation, or consistent rationale writing for LLM outputs, that aligns well with the role.","Do I need prior RLHF experience?",{"A":23,"Q":24},"Emphasize data labeling, annotation guidelines compliance, QA evaluation, prompt evaluation, RLHF familiarity, training data quality practices, named entity recognition, computer vision annotation, and content safety labeling.","What skills should I emphasize to be competitive?",{"A":26,"Q":27},"Hiring demand typically comes from AI labs, technology companies, tech startups, annotation vendors, and enterprise teams that need scalable data annotation and evaluation capacity.","Who hires for these roles through Rex.zone?",{"A":29,"Q":30},"The salary range is USD 63360 to 126720 per YEAR.","What is the salary range and pay period?","senior-data-labeling-jobs-united-states",{"desc":33,"title":34,"content":35},"Senior data labeling professionals turn raw text, images, audio, and video into high-quality training data for modern AI systems. At Rex.zone, these remote roles support LLM training pipelines through data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation to improve model performance, reliability, and content safety. You will apply annotation guidelines compliance, resolve ambiguity with clear decision logs, and drive training data quality at scale across NLP, computer vision, and multimodal workflows. Explore full-time remote opportunities in the United States and help AI labs, tech startups, annotation vendors, and enterprise teams ship safer, more accurate models.","Senior Data Labeling Jobs in the United States (Remote)",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Heading: Senior Data Labeling Specialist (United States — Remote, Full-Time)","Title: Senior Data Labeling Specialist (United States — Remote)\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: Data Labeling, Data Annotation, RLHF, LLM Evaluation, QA Evaluation, Prompt Evaluation, Annotation Guidelines, Training Data Quality, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will lead complex data labeling and evaluation tasks that directly influence large language model evaluation, model performance improvement, and training data quality. This includes RLHF-style preference ranking, prompt evaluation, and QA evaluation across text, code, and multimodal content. You will interpret annotation guidelines, document edge cases, and collaborate with project leads to reduce ambiguity and improve inter-annotator agreement. Work is fully Remote within the United States and aligned with full-time delivery expectations.",{"h2":43,"desc":44},"What You Will Do","Core responsibilities include: (1) perform senior-level data labeling and data annotation for NLP and computer vision annotation workflows; (2) execute RLHF preference comparisons, rationale capture, and policy-consistent rankings; (3) run QA evaluation audits, identify systematic errors, and propose guideline updates; (4) conduct prompt evaluation to assess helpfulness, accuracy, tone, and safety; (5) label and review content safety labeling categories such as harassment, hate, self-harm, sexual content, and violence; (6) support named entity recognition tasks and taxonomy expansion; (7) track training data quality metrics (agreement rates, error types, rework rates) and escalate recurring failure modes; (8) maintain detailed decision logs to improve reviewer consistency and model training outcomes.",{"h2":46,"desc":47},"Required Qualifications","We are looking for: (1) demonstrated experience in data labeling, annotation operations, or AI\u002FML evaluation work; (2) strong ability to follow annotation guidelines compliance and apply consistent judgment on edge cases; (3) familiarity with RLHF concepts, preference ranking, or human feedback loops for LLM training pipelines; (4) experience with QA evaluation, error categorization, and corrective feedback; (5) comfort working with sensitive content in content safety labeling contexts; (6) excellent written communication for rationale writing and audit trails; (7) ability to work independently in a Remote environment in the US while meeting full-time throughput and quality targets.",{"h2":49,"desc":50},"Preferred Qualifications","Nice to have: (1) hands-on exposure to named entity recognition, intent classification, or document labeling; (2) computer vision annotation experience (bounding boxes, polygons, keypoints, segmentation masks); (3) experience evaluating LLM outputs for factuality, reasoning, and instruction-following; (4) prior work with annotation vendors, BPO teams, or multi-reviewer workflows; (5) familiarity with calibration sessions, adjudication, and rubric design; (6) understanding of prompt evaluation frameworks and red teaming basics.",{"h2":52,"desc":53},"Tools and Workflows","You may use web-based labeling tools and structured rubrics for data labeling, RLHF ranking, and QA evaluation. Typical workflows include guideline onboarding, calibration, production labeling, peer review, adjudication, and continuous guideline iteration. Emphasis is placed on traceability, consistency, and measurable training data quality improvements.",{"h2":55,"desc":56},"Compensation and Schedule","This is a FULL_TIME Remote role based in the US. Compensation range is USD 63360 to 126720 per YEAR, depending on scope, domain complexity (NLP, computer vision, content safety), and demonstrated evaluation accuracy.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your profile and highlighting senior data labeling experience, QA evaluation exposure, and any RLHF or prompt evaluation work. If available, include examples of guideline interpretation, edge-case documentation, and how you improved training data quality or reviewer agreement.","AI Data Operations"]