[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotator-jobs-los-angeles":3},{"Ques":4,"Slug":37,"Header":38,"job_category":72},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28,31,34],{"A":8,"Q":9},"Yes. Remote Type is Remote, and the work is performed remotely even though the hiring focus includes Los Angeles candidates.","Are these remote data annotator jobs in Los Angeles fully remote?",{"A":11,"Q":12},"The Employment Type is FULL_TIME. Some projects may include contract or freelance workflows on the platform, but this posting is for full-time remote work.","Is this a full-time role or contract\u002Ffreelance?",{"A":14,"Q":15},"Common tasks include data labeling for NLP and computer vision, named entity recognition, prompt evaluation, QA evaluation, content safety labeling, and RLHF preference ranking for large language model evaluation.","What kinds of tasks are included in data annotation for AI\u002FML?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) uses human preferences and rubric-based judgments to improve model behavior. Annotators create preference and evaluation data that supports model performance improvement in LLM training pipelines.","What is RLHF and why is it part of data annotation?",{"A":20,"Q":21},"Skills should align with remote data annotation and AI training workflows, including Data Annotation, Data Labeling, RLHF, LLM Evaluation, Prompt Evaluation, QA Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, Annotation Guidelines Compliance, Training Data Quality, and LLM Training Pipelines.","What skills should match this role’s intent?",{"A":23,"Q":24},"Experience Level is Mid-Senior, meaning you should be comfortable operating independently, handling edge cases, and contributing to quality processes.","Which experience level is targeted?",{"A":26,"Q":27},"Industry is Technology. Work commonly supports AI labs, tech startups, BPOs, and annotation vendors building products across NLP, computer vision, and content safety.","What industries and employer types does this work support?",{"A":29,"Q":30},"Quality is measured through annotation guidelines compliance, inter-annotator agreement, audit defect rates, QA evaluation results, and the impact on training data quality for downstream model evaluation.","How is quality measured in annotation work?",{"A":32,"Q":33},"Salary Currency is USD with Salary Min 63360 and Salary Max 126720 per YEAR.","What is the salary range for this posting?",{"A":35,"Q":36},"Apply on Rex.zone by submitting your profile and completing required assessments or calibration tasks. After passing, you’ll join production labeling with ongoing QA feedback.","How do I apply via Rex.zone?","remote-data-annotator-jobs-los-angeles",{"desc":39,"title":40,"content":41},"Remote Data Annotator Jobs in Los Angeles at Rex.zone focus on training data creation for AI\u002FML systems, including data labeling, RLHF evaluation, and QA checks that improve large language model evaluation and model performance improvement. You will apply annotation guidelines compliance to text, image, and multimodal tasks such as named entity recognition, prompt evaluation, content safety labeling, and computer vision annotation. This role connects directly to LLM training pipelines and training data quality, supporting AI labs, tech startups, and annotation vendors. Explore and apply through Rex.zone to join a distributed team delivering consistent, auditable labels that power reliable AI products.","Remote Data Annotator Jobs in Los Angeles",[42,45,48,51,54,57,60,63,66,69],{"h2":43,"desc":44},"Remote Data Annotator Jobs in Los Angeles — Overview","Title: Remote Data Annotator Jobs in Los Angeles\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 Annotation, Data Labeling, RLHF, LLM Evaluation, Prompt Evaluation, QA Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, Annotation Guidelines Compliance, Training Data Quality, LLM Training Pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":46,"desc":47},"About the Role","As a Remote Data Annotator supporting Los Angeles-area hiring needs, you will produce high-quality labeled datasets used to train and evaluate AI models. Your work may include LLM response ranking for RLHF, prompt evaluation for instruction-following, QA evaluation for edge cases, and content safety labeling for policy compliance. You will also complete structured annotation for NLP tasks (named entity recognition, intent classification) and computer vision annotation (bounding boxes, polygons, keypoints) depending on project needs. Success is measured by training data quality, annotation consistency, throughput, and adherence to annotation guidelines compliance.",{"h2":49,"desc":50},"What You Will Do","You will execute end-to-end annotation workflows across multiple domains, aligned to real AI\u002FML training pipelines.\nResponsibilities include:\n- Apply data labeling rules to text, image, audio, or multimodal samples.\n- Perform RLHF tasks such as preference ranking, pairwise comparison, and rubric-based scoring.\n- Conduct QA evaluation: self-checks, peer review, and targeted audits to reduce label noise.\n- Perform named entity recognition and other NLP labeling with consistent span selection.\n- Support prompt evaluation by assessing relevance, factuality, instruction-following, and safety.\n- Complete computer vision annotation (bounding boxes, segmentation masks) when assigned.\n- Document edge cases, propose guideline clarifications, and follow versioned labeling specs.\n- Track annotation guidelines compliance and escalate ambiguous examples for resolution.",{"h2":52,"desc":53},"Required Qualifications","To succeed in these remote data annotator jobs, you should bring strong analytical judgment and consistency.\nQualifications:\n- Mid-Senior experience in data annotation, data labeling, QA evaluation, or related operations.\n- Familiarity with LLM evaluation concepts (rubrics, preference data, error taxonomies).\n- Experience following detailed annotation guidelines compliance and change logs.\n- Strong written communication for documenting rationale and edge cases.\n- Comfort working with web-based labeling tools and spreadsheet-like QA trackers.\n- Ability to maintain training data quality under throughput targets and tight feedback cycles.",{"h2":55,"desc":56},"Preferred Qualifications (Nice to Have)","These skills help you contribute across more project types and employer contexts.\nNice to have:\n- Hands-on RLHF experience (ranking, critique writing, reward model data collection).\n- NLP labeling experience: named entity recognition, sentiment, intent, or taxonomy tagging.\n- Computer vision annotation experience: polygons, segmentation, and occlusion handling.\n- Content safety labeling experience (policy-driven classification and moderation taxonomies).\n- Exposure to evaluation metrics and error analysis tied to model performance improvement.\n- Experience working with AI labs, tech startups, BPOs, or annotation vendors.",{"h2":58,"desc":59},"Workflow, Quality, and Performance","You will follow repeatable processes designed to maximize label reliability.\nKey workflow elements:\n- Calibrations: read guidelines, complete quizzes, and align on gold-standard examples.\n- Production labeling: annotate with consistency and maintain clear reasoning notes.\n- QA loops: spot checks, disagreement resolution, and systematic error correction.\n- Measurement: track agreement rates, defect categories, and training data quality indicators.\n- Continuous improvement: propose guideline updates that reduce ambiguity and rework.",{"h2":61,"desc":62},"Tools and Data Types","Projects vary, but typical data types and tooling include:\n- Text: prompts, chat turns, documents, short-form queries for LLM training pipelines.\n- Images\u002Fvideo: objects, scenes, and attributes for computer vision annotation.\n- Safety datasets: policy categories and severity scales for content safety labeling.\n- Tooling: web-based labeling platforms, rubric forms, QA evaluation dashboards, and audit logs.",{"h2":64,"desc":65},"Employment Details","This is a Remote, FULL_TIME role aligned to Los Angeles recruitment needs while remaining fully remote.\nYou will collaborate asynchronously with distributed project teams and follow defined schedules for calibration, production, and QA review.",{"h2":67,"desc":68},"Compensation","Salary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":70,"desc":71},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your profile and completing any required screening tasks.\nTypical steps:\n- Create or update your Rex.zone profile.\n- Complete a short skills and guideline comprehension assessment.\n- Pass calibration tasks and begin production with ongoing QA feedback.\nRex.zone supports multiple employer types (AI labs, tech startups, BPOs, annotation vendors) and domain types (NLP, computer vision, content safety, LLM training).","AI Data Operations"]