Remote Jobs Remote — AI/ML 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/ML 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.

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About These Roles

These remote-first roles support end-to-end AI/ML 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.

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.

AI/ML 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.

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/annotation environments, you’ll quickly adapt to our workflows.

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.

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.

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.

Frequently Asked Questions

  • Q: 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: How do these roles connect to real AI/ML 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: 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: 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: 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: 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: 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: 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.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated—it's properly compensated.

Work Remotely, Work Freely

No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks—we handle the rest.

Respect at the Core of Everything

AI trainers are the heart of our company. We treat every expert with trust, humanity, and genuine appreciation. From personalized support to transparent communication, we build long-term relationships rooted in respect and care.

Ready to Shape the Future of Remote AI/ML Annotation, Evaluation & RLHF Jobs?

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