[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotation-jobs-los-angeles":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 Remote and can be performed from Los Angeles while collaborating with distributed teams through Rex.zone.","Are these remote data annotation jobs in Los Angeles fully remote?",{"A":11,"Q":12},"This posting is FULL_TIME. Rex.zone may also list contract and freelance roles on the platform, but this specific opening is full-time remote.","Is this job full-time or contract\u002Ffreelance?",{"A":14,"Q":15},"Common tasks include data labeling for NLP and LLM training, RLHF preference ranking, prompt evaluation, QA evaluation and auditing, named entity recognition, computer vision annotation, and content safety labeling.","What types of annotation tasks are included?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) involves collecting human preference signals—such as ranking outputs or selecting better responses—to improve model alignment and response quality during LLM training pipelines.","What does RLHF mean in this role?",{"A":20,"Q":21},"Quality is measured through annotation guidelines compliance, calibration performance, disagreement rates, audit accuracy, and downstream signals like model performance improvement on evaluation sets.","How is quality measured for training data?",{"A":23,"Q":24},"An AI\u002FML degree is not required. Demonstrated experience with data annotation, data labeling, evaluation workflows, and careful guideline-based decision-making is most important.","Do I need an AI\u002FML degree to apply?",{"A":26,"Q":27},"Projects commonly span NLP, computer vision, content safety, and LLM evaluation. Employer types can include AI labs, tech startups, BPOs, and annotation vendors.","What domains might I work on?",{"A":29,"Q":30},"The listed Experience Level is Mid-Senior. Rex.zone may host entry-level and senior roles as separate postings, but this specific posting targets experienced annotators.","Is this role entry-level or senior?","remote-data-annotation-jobs-los-angeles",{"desc":33,"title":34,"content":35},"Rex.zone is hiring for remote data annotation jobs in Los Angeles focused on training data creation and evaluation for modern AI systems. As a Data Annotation Specialist, you will label and review text, images, audio, and conversations used in LLM training pipelines, RLHF workflows, and model evaluation. Your work improves training data quality, annotation guidelines compliance, and model performance improvement across NLP, computer vision annotation, content safety labeling, and prompt evaluation. This is a full-time remote role aligned to real production workflows used by AI labs, tech startups, and annotation vendors—apply through Rex.zone to join a distributed data operations team.","Remote Data Annotation Jobs in Los Angeles",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Opening: Remote Data Annotation Specialist (Los Angeles)","Title: Remote 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: data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will produce high-quality labeled datasets and evaluation signals that support large language model training pipelines. Tasks include text and chat annotation, prompt evaluation, QA evaluation, content safety labeling, and structured labeling such as named entity recognition. You will follow detailed annotation guidelines, document edge cases, and collaborate with QA to reduce disagreement rates and improve labeling consistency across projects used by AI labs, tech startups, BPOs, and annotation vendors.",{"h2":43,"desc":44},"What You Will Do","Core responsibilities include: (1) Perform data labeling for NLP, computer vision annotation, and multimodal tasks (text-image). (2) Execute RLHF-style ranking and preference judgments to improve model alignment and response quality. (3) Run LLM evaluation tasks including rubric-based scoring, prompt-response grading, and error categorization. (4) Apply content safety labeling policies for toxicity, self-harm, harassment, and sensitive attributes. (5) Conduct QA evaluation: audit samples, compute issue rates, and propose guideline clarifications. (6) Track annotation guidelines compliance and escalate ambiguous cases with clear examples.",{"h2":46,"desc":47},"Workflow and Tools","You will work in web-based annotation platforms and internal tooling typical of AI data operations teams. You will use task instructions, gold-standard examples, and calibration sets; maintain review notes for disagreements; and support continuous improvement of training data quality. Familiarity with labeling taxonomies, rubric design, inter-annotator agreement, and dataset versioning is helpful.",{"h2":49,"desc":50},"Required Qualifications","Mid-Senior experience in structured data annotation, data labeling, QA evaluation, or model evaluation. Strong attention to detail with consistent decision-making under guidelines. Ability to read and apply labeling policies for content safety labeling and sensitive content. Comfort working with ambiguity and documenting edge cases. Reliable remote work habits, including time management and clear written communication.",{"h2":52,"desc":53},"Preferred Qualifications","Experience with RLHF or preference ranking tasks; LLM evaluation and prompt evaluation; named entity recognition (NER) or taxonomy-based labeling; computer vision annotation (bounding boxes, segmentation, keypoints); and quality programs such as auditing, calibration sessions, and disagreement analysis. Exposure to AI\u002FML training pipelines, dataset curation, and model performance improvement loops is a plus.",{"h2":55,"desc":56},"Why Rex.zone","Rex.zone connects qualified remote annotators with real AI production work across domains like NLP, computer vision, LLM training, and content safety. You will contribute to measurable improvements in training data quality and model evaluation outcomes while working full-time in a remote-first environment.",{"h2":58,"desc":59},"How to Apply","Apply via Rex.zone with a resume highlighting annotation experience, QA evaluation work, and any RLHF, LLM evaluation, named entity recognition, computer vision annotation, or content safety labeling projects. Include examples of guideline-driven decision-making, audit\u002Freview experience, and training data quality improvements.","AI Data Operations"]