[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-data-annotation-jobs-los-angeles":3},{"Ques":4,"Slug":31,"Header":32,"job_category":59},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. These roles are Remote and open to qualified candidates based in the US, including Los Angeles, while the work itself is performed remotely through Rex.zone project workflows.","Are these truly remote AI data annotation jobs for Los Angeles?",{"A":11,"Q":12},"Work commonly includes data labeling for NLP tasks, named entity recognition, prompt evaluation for LLM outputs, RLHF preference labeling, QA evaluation, computer vision annotation, and content safety labeling depending on the project.","What kind of annotation work is included?",{"A":14,"Q":15},"This posting is for FULL_TIME employment. Some teams in the ecosystem also run contract or freelance projects, but this role is explicitly full-time and remote.","Is this contract or full-time?",{"A":17,"Q":18},"The role is Mid-Senior. You should be comfortable applying annotation guidelines consistently, handling edge cases, and supporting training data quality through QA evaluation.","What experience level is expected?",{"A":20,"Q":21},"RLHF requires human feedback data such as preference rankings, comparisons, and rubric-based evaluations of model outputs. This role may involve creating or reviewing those labels to improve model behavior and safety.","How does RLHF relate to data annotation?",{"A":23,"Q":24},"Training data quality directly impacts model performance improvement, reliability, and safety. Consistent labels, clear guideline compliance, and strong QA evaluation reduce noise and bias in model training pipelines.","What makes training data quality important?",{"A":26,"Q":27},"Projects may span NLP, computer vision annotation, content safety labeling, and LLM evaluation. Employer types can include AI labs, tech startups, BPOs, and annotation vendors sourced through Rex.zone.","What domains might I work in?",{"A":29,"Q":30},"Highlight AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, and any experience with named entity recognition, computer vision annotation, or content safety labeling.","What skills should I highlight to be competitive?","remote-ai-data-annotation-jobs-los-angeles",{"desc":33,"title":34,"content":35},"Remote AI data annotation jobs in Los Angeles focus on training-data creation for modern AI systems. At Rex.zone, you will label and review text, images, audio, and multimodal samples used in LLM training pipelines, RLHF (Reinforcement Learning from Human Feedback), and model evaluation. The work includes data labeling, prompt evaluation, QA evaluation, and annotation guidelines compliance to improve model performance, training data quality, and safety behavior. These full-time remote roles support projects across NLP, computer vision annotation, content safety labeling, and named entity recognition for AI labs, tech startups, BPOs, and annotation vendors—while staying fully remote in the US.","Remote AI Data Annotation Jobs in Los Angeles",[36,38,41,44,47,50,53,56],{"h2":34,"desc":37},"Title: Remote AI Data Annotation Specialist (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: AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, content safety labeling, model performance improvement\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","You will perform remote AI data annotation for projects tied to real-world AI\u002FML training workflows, including LLM training pipelines, RLHF preference data, and evaluation datasets. You will apply detailed annotation guidelines, produce consistent labels, and conduct QA evaluation to ensure training data quality that supports model performance improvement. Tasks may span NLP text labeling, named entity recognition, prompt evaluation, content safety labeling, and computer vision annotation depending on project needs.",{"h2":42,"desc":43},"Key Responsibilities","You will label and review text, image, audio, and multimodal data for supervised learning and LLM fine-tuning; create RLHF preference comparisons and rationale when required; perform prompt evaluation and response grading across quality, relevance, and safety; follow annotation guidelines compliance and document edge cases; run QA evaluation workflows (self-checks, peer review, consensus); identify ambiguity, propose guideline clarifications, and escalate data issues; track throughput, accuracy, and consistency to maintain training data quality; support dataset curation for NLP, computer vision, and content safety labeling projects.",{"h2":45,"desc":46},"Required Qualifications","Mid-to-senior experience in AI data annotation, data labeling, or ML data operations; strong understanding of annotation guidelines compliance and quality control; experience with LLM evaluation, RLHF, or preference ranking is strongly valued; familiarity with NLP concepts (classification, named entity recognition) and\u002For computer vision annotation (bounding boxes, segmentation) is a plus; ability to apply consistent judgment across edge cases and write clear issue notes; comfort working in a remote, metrics-driven production workflow.",{"h2":48,"desc":49},"Tools and Data Types You May Work With","Annotation platforms and review queues; text classification and named entity recognition; prompt evaluation rubrics for LLM outputs; RLHF preference labeling; computer vision annotation such as bounding boxes, polygons, segmentation masks; content safety labeling across policy categories; QA evaluation methods such as gold tasks, audits, and inter-annotator agreement.",{"h2":51,"desc":52},"Why Rex.zone","Rex.zone connects remote professionals to full-time opportunities supporting AI training pipelines for AI labs, tech startups, BPOs, and annotation vendors. You will work on production datasets that influence model behavior, training data quality, and safety outcomes, while operating in a fully remote environment based in the US.",{"h2":54,"desc":55},"Employment Details","This is a FULL_TIME, Remote role in the US. Work is performed remotely while aligned to project schedules and quality targets. Compensation is listed as annual USD salary range.",{"h2":57,"desc":58},"How to Apply","Apply through Rex.zone with a resume highlighting AI data annotation, data labeling, RLHF\u002FLLM evaluation, QA evaluation, and any domain experience in NLP, computer vision annotation, or content safety labeling. Include examples of guideline-driven work and how you maintained training data quality and model performance improvement outcomes.","AI Data Operations"]