[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-jobs-remote":3},{"Slug":4,"job_category":5,"MetaTitle":6,"MetaDescription":7,"Header":8,"OpenPositions":33,"Sections":207,"ApplyCTA":245,"Ques":261,"SEO":288},"remote jobs remote","Remote AI\u002FML Annotation, Evaluation & RLHF Jobs","remote jobs remote | 2026 Remote jobs","remote jobs remote — top LLM training and data labeling roles on Rex.zone. Explore freelance, contract, and full-time remote AI jobs in 2026.",{"title":9,"desc":10,"content":11},"Remote Jobs Remote — AI\u002FML Data Labeling, RLHF, and Evaluation Roles at Rex.zone","remote jobs remote at Rex.zone connects experienced and aspiring contributors with high-impact roles in AI\u002FML training workflows. This page aggregates remote, contract, freelance, full-time, entry-level, and senior opportunities across RLHF (Reinforcement Learning from Human Feedback), data labeling, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines. Our hiring focus spans AI labs, tech startups, BPOs, and annotation vendors that rely on training data quality, annotation guidelines compliance, and rigorous large language model evaluation to drive model performance improvement. Apply now on Rex.zone to join human-in-the-loop teams shaping next-generation AI systems.",[12,15,18,21,24,27,30],{"h2":13,"desc":14},"About These Roles","These remote-first roles support end-to-end AI\u002FML development, from raw data curation to model scorecards. You will work within structured workflows—labeling, validating, and evaluating datasets and model outputs—so downstream teams can strengthen model reliability and safety. Projects cover NLP, computer vision, and multimodal tasks, including entity tagging, sentiment analysis, summarization grading, prompt evaluation, pairwise preference collection for RLHF, bounding box and polygon annotation, segmentation, quality audits, and policy-aligned content safety labeling.",{"h2":16,"desc":17},"Why Rex.zone","Rex.zone is a hiring gateway trusted by AI labs and startups for human-in-the-loop excellence. We standardize annotation guidelines, quality rubrics, and evaluation harnesses; align contributor pools by domain (NLP, computer vision, content safety); and provide transparent remote jobs remote pipelines with clear advancement paths (entry-level to senior reviewer). Candidates benefit from streamlined onboarding, tool access, and consistent feedback loops to maintain annotation guidelines compliance and improve model performance over time.",{"h2":19,"desc":20},"AI\u002FML Workflow Coverage","Our remote jobs remote catalog taps into core training workflows: data labeling and enrichment; gold set creation; adversarial test crafting; prompt evaluation and ranking; RLHF preference data collection; instruction following assessment; toxicity, bias, and hallucination audits; regression testing; and model scorecard reporting. You will use annotation tools and SDKs (Label Studio, Prodigy, CVAT, custom labeling UIs, Python notebooks), follow SOPs and policy taxonomies, and collaborate with QA leads to ensure training data quality across iterative model releases.",{"h2":22,"desc":23},"Who Thrives Here","Ideal candidates combine detail orientation with practical ML intuition. You’re comfortable interpreting ambiguous instructions, asking clarifying questions, and applying consistent judgment across high-volume tasks. Strong writing for prompt evaluation and rubric-based scoring is essential in RLHF and LLM evaluation roles, while vision annotators need spatial reasoning for precise segmentation. If you’ve worked in LLM training pipelines, editorial QA, content moderation, or crowdsourcing\u002Fannotation environments, you’ll quickly adapt to our workflows.",{"h2":25,"desc":26},"Search Modifiers We Support","We actively recruit for remote, contract, freelance, and full-time roles. Both entry-level and senior openings are available. Domain focus areas include NLP, computer vision, content safety, and LLM training. Employers range from AI labs and tech startups to BPOs and annotation vendors.",{"h2":28,"desc":29},"Example Responsibilities","Responsibilities vary by role but commonly include: creating and refining labeling guidelines; executing large-scale annotations with high precision; conducting spot checks and inter-annotator agreement analysis; documenting edge cases; performing pairwise comparisons for model preference data; generating adversarial prompts and test cases; compiling evaluation reports; and recommending data-driven improvements to model behavior.",{"h2":31,"desc":32},"Impact and Outcomes","Your work directly influences model safety, helpfulness, and robustness. By delivering consistent annotations and reliable evaluation signals, you enable model performance improvement across public benchmarks and internal metrics. High-quality training data and disciplined QA evaluation translate into more trustworthy and capable AI systems for production use.",[34,61,82,104,126,147,168,188],{"title":35,"location":36,"employment_type":37,"domains":38,"compensation":43,"description":44,"responsibilities":45,"requirements":51,"nice_to_have":56,"apply_url":60},"RLHF Human Rater — LLM Preference Evaluator","Remote","Contract \u002F Part-time",[39,40,41,42],"NLP","LLM Training","RLHF","Prompt Evaluation","USD $18–30 per hour, experience-dependent","Evaluate model outputs against prompts using detailed rubrics. Perform pairwise comparisons, label rationales, and flag policy violations to support RLHF pipelines. Contribute to large language model evaluation with emphasis on helpfulness, honesty, and harmlessness.",[46,47,48,49,50],"Conduct prompt evaluation and pairwise preference tasks with clear rationales","Apply policy taxonomies for content safety labeling and escalation","Track inter-annotator agreement and provide feedback for rubric calibration","Draft adversarial prompts to probe model weaknesses","Summarize evaluation findings for model scorecards",[52,53,54,55],"Exceptional written English and rubric-based judgment","Familiarity with RLHF concepts and LLM failure modes (hallucination, bias, toxicity)","Reliable home office setup and time management for remote work","Ability to follow annotation guidelines compliance rigorously",[57,58,59],"Experience with human-in-the-loop systems and evaluation harnesses","Exposure to prompt engineering and A\u002FB testing","Multi-language proficiency for cross-lingual evaluation","https:\u002F\u002Frex.zone\u002Fapply\u002Frlhf-human-rater",{"title":62,"location":36,"employment_type":63,"domains":64,"compensation":67,"description":68,"responsibilities":69,"requirements":74,"nice_to_have":78,"apply_url":81},"Data Labeling Specialist — NLP (NER, Sentiment, Classification)","Full-time \u002F Contract",[39,65,66],"Data Labeling","QA Evaluation","USD $22–35 per hour or salary equivalent","Execute named entity recognition, sentiment analysis, topical classification, and span labeling tasks. Maintain training data quality through SOPs, audits, and inter-annotator agreement tracking.",[70,71,72,73],"Label large text corpora with precise NER tags and sentiment categories","Document edge cases and propose guideline clarifications","Run QA spot checks and participate in calibration sessions","Collaborate with leads on model performance improvement via error analysis",[75,76,77],"Experience with labeling platforms (Label Studio, Prodigy) or similar","Detail orientation and consistency across high-volume tasks","Comfort with spreadsheet QA and basic scripting for checks (optional)",[79,80],"Domain-specific tagging experience (legal, medical, finance)","Regex familiarity and basic Python for data sanity checks","https:\u002F\u002Frex.zone\u002Fapply\u002Fnlp-labeling-specialist",{"title":83,"location":36,"employment_type":84,"domains":85,"compensation":89,"description":90,"responsibilities":91,"requirements":96,"nice_to_have":100,"apply_url":103},"Computer Vision Annotator — Bounding Box & Segmentation","Freelance \u002F Contract",[86,87,88],"Computer Vision","Annotation","Quality Assurance","Task-based rates or USD $18–28 per hour","Create pixel-accurate annotations (boxes, polygons, keypoints). Uphold annotation guidelines compliance to guarantee training data quality for detection and segmentation models.",[92,93,94,95],"Annotate images\u002Fvideo using bounding boxes, polygons, and keypoints","Follow class taxonomies and maintain consistent label naming","Perform QA evaluation with double-pass reviews and disagreements resolution","Report annotation throughput and quality metrics",[97,98,99],"Prior experience with CVAT, SuperAnnotate, or similar tools","Excellent visual precision and understanding of occlusions\u002Fedges","Stable internet and Wacom\u002Ftablet familiarity (preferred)",[101,102],"LiDAR\u002F3D annotation exposure","Experience with medical or geospatial imagery","https:\u002F\u002Frex.zone\u002Fapply\u002Fcomputer-vision-annotator",{"title":105,"location":36,"employment_type":106,"domains":107,"compensation":111,"description":112,"responsibilities":113,"requirements":118,"nice_to_have":122,"apply_url":125},"Content Safety Labeler — Policy & Risk","Part-time \u002F Full-time",[108,109,110],"Content Safety","Policy","LLM Evaluation","USD $20–32 per hour, shift-based","Apply safety taxonomies to classify and escalate sensitive content. Support policy-aligned labeling for LLM training and evaluation, improving safe response generation.",[114,115,116,117],"Classify content according to harm categories and severity levels","Annotate rationales; escalate ambiguous or high-risk items","Contribute to training sets used for policy classifier fine-tuning","Participate in calibration sessions to reduce false positives\u002Fnegatives",[119,120,121],"Resilience and policy comprehension; clear ethical judgment","Ability to maintain consistency across nuanced edge cases","Experience in moderation, trust & safety, or compliance",[123,124],"Multilingual moderation experience","Familiarity with PII redaction and privacy frameworks","https:\u002F\u002Frex.zone\u002Fapply\u002Fcontent-safety-labeler",{"title":127,"location":36,"employment_type":128,"domains":129,"compensation":132,"description":133,"responsibilities":134,"requirements":139,"nice_to_have":143,"apply_url":146},"Prompt Evaluation Contractor — Instruction Following","Freelance \u002F Project-based",[110,130,131],"Prompting","QA","USD $25–40 per hour","Assess instruction-following quality and response helpfulness using structured rubrics. Generate challenging test prompts and curate gold standards to benchmark models.",[135,136,137,138],"Design prompts and adversarial examples covering corner cases","Score responses with consistent rationale and references","Analyze failure patterns and propose rubric improvements","Help maintain evaluation harnesses and scorecards",[140,141,142],"Excellent writing and critical reasoning","Prior experience rating text quality or editorial QA","Comfort with structured rubrics and evidence-based scoring",[144,145],"Scripting skills for data analysis","Familiarity with benchmark suites and eval libraries","https:\u002F\u002Frex.zone\u002Fapply\u002Fprompt-evaluation-contractor",{"title":148,"location":36,"employment_type":149,"domains":150,"compensation":153,"description":154,"responsibilities":155,"requirements":160,"nice_to_have":164,"apply_url":167},"QA Evaluation Lead — Data Quality & Guidelines","Full-time",[88,151,152],"Process","Data Operations","USD $70k–$110k per year","Own guidelines, calibration, and quality metrics. Drive training data quality through audits, feedback loops, and analytics across multi-domain labeling programs.",[156,157,158,159],"Author and version annotation guidelines and policy taxonomies","Run calibration sessions; track inter-annotator agreement (IAA)","Lead root-cause analysis for quality regressions","Report on precision\u002Frecall and business impact to stakeholders",[161,162,163],"3+ years in data quality or annotation operations","Analytical skills; comfort with dashboards and sampling methodology","Strong communication across vendors and internal teams",[165,166],"Experience with BPOs or large-scale annotation vendors","Background in ML data operations and program management","https:\u002F\u002Frex.zone\u002Fapply\u002Fqa-evaluation-lead",{"title":169,"location":36,"employment_type":170,"domains":171,"compensation":173,"description":174,"responsibilities":175,"requirements":180,"nice_to_have":184,"apply_url":187},"LLM Training Pipeline Coordinator","Full-time \u002F Contract-to-Hire",[152,40,172],"Process Engineering","USD $80k–$130k per year","Coordinate cross-functional tasks from data ingestion to evaluation release. Align human feedback collection, dataset versioning, and model evaluation sprints.",[176,177,178,179],"Define SOPs for data labeling, reviews, and release gates","Coordinate RLHF collection, gold set curation, and regression checks","Maintain dashboards on cycle time, quality, and throughput","Interface with AI labs, startups, and annotation vendors",[181,182,183],"Program management experience in data\u002FML operations","Familiar with evaluation harnesses and LLM lifecycle","Strong stakeholder management and documentation skills",[185,186],"SQL\u002FPython for analytics automation","Experience with MLOps tooling and dataset governance","https:\u002F\u002Frex.zone\u002Fapply\u002Fllm-training-pipeline-coordinator",{"title":189,"location":36,"employment_type":190,"domains":191,"compensation":192,"description":193,"responsibilities":194,"requirements":199,"nice_to_have":203,"apply_url":206},"Entry-Level Annotation Associate","Entry-level \u002F Part-time or Full-time",[39,86,108],"USD $15–22 per hour","Kickstart your career in AI with foundational labeling tasks. Learn guidelines, quality checks, and remote collaboration while contributing to real-world datasets.",[195,196,197,198],"Complete labeling tasks per SOPs and submit for review","Participate in calibration and feedback sessions","Escalate ambiguous cases to senior reviewers","Maintain high annotation accuracy and throughput",[200,201,202],"Reliable internet and attention to detail","Comfort following written instructions and feedback","Availability for remote shifts and deadline adherence",[204,205],"Prior gig work in search quality or data entry","Basic familiarity with labeling tools","https:\u002F\u002Frex.zone\u002Fapply\u002Fentry-annotation-associate",[208,217,227,236],{"h2":209,"p":210,"bullets":212},"How We Hire on Rex.zone",[211],"Our remote jobs remote pipeline emphasizes clarity, fairness, and speed. After you apply, you may receive a brief skills assessment aligned with the role (NER snippet, bounding box sample, or rubric-based evaluation task). We use these results to calibrate expectations and place you on the right projects. Next, successful candidates proceed to a short interview focused on workflow understanding, annotation guidelines compliance, and communication habits crucial for remote collaboration. Final onboarding includes tool access, SOP training, and a paid pilot to validate quality before scaling to full production.",[213,214,215,216],"Transparent role descriptions and scope","Paid pilots to verify training data quality","Fast feedback cycles and clear acceptance criteria","Advancement paths from entry-level to senior QA",{"h2":218,"p":219,"bullets":222},"Tools, Methods, and Quality Standards",[220,221],"We align projects to toolchains that sustain accuracy and throughput. NLP teams use Label Studio, Prodigy, or custom UIs for named entity recognition, classification, and span labeling. Computer vision uses CVAT, SuperAnnotate, or internal tools for boxes, polygons, and keypoints. RLHF and prompt evaluation rely on proprietary evaluation harnesses that log pairwise preference decisions, rationales, and inter-annotator agreement. Quality is driven by double-pass reviews, gold sets, sampling plans, and dashboards that tie annotation decisions to model performance improvement. This ensures that large language model evaluation data remains traceable, reproducible, and audit-ready.","Security and privacy are non-negotiable: secure SSO, least-privilege access, PII redaction, and encrypted data paths. We uphold confidentiality agreements with AI labs, tech startups, BPOs, and annotation vendors to protect datasets and model outputs at all times.",[223,224,225,226],"Double-pass reviews and IAA tracking","Gold set curation and rubric calibration","A\u002FB tests for guideline and prompt variants","Secure data handling with audit logs",{"h2":228,"p":229,"bullets":231},"Career Growth and Work Modes",[230],"Whether you prefer freelance flexibility or full-time stability, our remote jobs remote ecosystem includes contract, freelance, and permanent roles. Many positions offer shift options for different time zones. Entry-level candidates gain structured training and feedback; experienced contributors can lead calibration sessions, author guidelines, or manage QA teams. Domain specialization—NLP, computer vision, content safety—opens pathways into lead roles or pipeline coordination. The diversity of employer types on Rex.zone (AI labs, startups, BPOs) lets you match your working style and industry interests with meaningful projects.",[232,233,234,235],"Freelance and contract gigs for flexible schedules","Full-time roles with benefits and growth tracks","Entry-level to senior paths, including QA lead","Cross-domain mobility across NLP, CV, and safety",{"h2":237,"p":238,"bullets":240},"Who Should Apply",[239],"We encourage applications from educators, editors, moderators, research assistants, linguists, UX raters, data entry pros, and ML-savvy technologists. If you have experience with search quality rating, content moderation, translation, or technical writing, you’ll find many skills transferable to RLHF, prompt evaluation, and labeling. We value clear communication, curiosity, and the ability to apply consistent criteria across diverse tasks. If you want to influence the next generation of AI safely and responsibly, this is your place.",[241,242,243,244],"Detail-oriented contributors with strong reading\u002Fwriting skills","Visual annotators with spatial precision and patience","Process-minded QA specialists who love metrics","Self-starters comfortable with remote collaboration",{"headline":246,"desc":247,"buttons":248},"Start Your Application on Rex.zone","Ready to join? Explore the open roles above or submit a general application to be considered for upcoming remote jobs remote across NLP, computer vision, content safety, and LLM training.",[249,252,255,258],{"label":250,"url":251},"Apply for RLHF & Prompt Evaluation","https:\u002F\u002Frex.zone\u002Fapply\u002Frlhf-track",{"label":253,"url":254},"Apply for NLP & Data Labeling","https:\u002F\u002Frex.zone\u002Fapply\u002Fnlp-track",{"label":256,"url":257},"Apply for Computer Vision","https:\u002F\u002Frex.zone\u002Fapply\u002Fcv-track",{"label":259,"url":260},"General Remote Talent Pool","https:\u002F\u002Frex.zone\u002Fapply\u002Fremote-talent",{"title":262,"content":263},"Frequently Asked Questions",[264,267,270,273,276,279,282,285],{"Q":265,"A":266},"What is the scope of remote jobs remote on Rex.zone?","We aggregate remote, contract, freelance, and full-time openings across RLHF, data labeling, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines for AI labs, tech startups, BPOs, and annotation vendors.",{"Q":268,"A":269},"How do these roles connect to real AI\u002FML workflows?","Your annotations and evaluations feed into training, fine-tuning, and release gates. By enforcing annotation guidelines compliance and producing high-quality labels and rubrics, you directly improve training data quality and enable measurable model performance improvement and reliable large language model evaluation.",{"Q":271,"A":272},"Are there entry-level opportunities?","Yes. Entry-level roles include supervised labeling tasks with SOP training, paid pilots, and feedback loops. Many contributors advance to reviewer or QA specialist roles as they master guidelines and quality metrics.",{"Q":274,"A":275},"What schedules are available?","We list roles with flexible schedules across multiple time zones, including part-time, shift-based, and full-time options. Freelance and contract projects are common for short-term or specialized needs.",{"Q":277,"A":278},"What tools will I use?","Common tools include Label Studio, Prodigy, CVAT, custom LLM evaluation harnesses for RLHF and prompt evaluation, secure portals for content safety labeling, and analytics dashboards for quality metrics.",{"Q":280,"A":281},"How does compensation work?","Compensation varies by domain and employer type, from hourly rates to task-based payments and full-time salaries. Projects specify pay ranges and performance-based incentives where applicable.",{"Q":283,"A":284},"Is training provided?","Yes. We provide SOPs, guideline documents, calibration sessions, and paid pilots so you can align with quality standards before entering production-scale tasks.",{"Q":286,"A":287},"How do I apply?","Click an apply link for specific roles or join the general talent pool on Rex.zone. You’ll complete a brief skills assessment aligned with the role and, if successful, proceed to interview and onboarding.",{"primary_keyword":4,"secondary_keywords":289,"internal_links":308,"notes":314},[290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307],"LLM training jobs","data labeling jobs remote","RLHF rater","prompt evaluation contractor","named entity recognition","computer vision annotation","content safety labeling","QA evaluation","training data quality","annotation guidelines compliance","model performance improvement","large language model evaluation","human-in-the-loop","freelance AI jobs","remote AI jobs","contract annotation roles","entry-level data labeling","senior QA lead",[309,312],{"label":310,"url":311},"Rex.zone Careers","https:\u002F\u002Frex.zone\u002Fcareers",{"label":313,"url":260},"Talent Pool","This page targets informational, transactional, and navigational intent by defining the role ecosystem, linking to applications on Rex.zone, and including common job modifiers and domain types."]