Remote STEM Jobs — AI/ML Data Labeling, RLHF & Evaluation

Remote STEM jobs are roles in science, technology, engineering, and mathematics delivered online, with a focus on AI/ML operations at Rex.zone. Candidates work on data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and RLHF tasks inside LLM training pipelines. The intent is to recruit talent across entry-level to senior for real-world workflows: training data quality audits, annotation guidelines compliance, model performance improvement, and large language model evaluation. Apply to remote, contract, freelance, or full-time openings, navigate employers on Rex.zone, and help build trustworthy AI systems. Roles span NLP, computer vision, data engineering, and safety operations. Join projects from AI labs, tech startups, BPOs, and annotation vendors.

About the Roles

These remote STEM roles map to recognized AI/ML job entities: data labeling specialists, RLHF evaluators, prompt evaluation analysts, named entity recognition annotators, computer vision labelers, content safety raters, and large language model evaluation experts. Work is delivered fully remote with flexible schedules across contract, freelance, and full-time engagements. Candidates contribute to structured annotation, rubric-driven QA, and iterative model feedback loops used by AI labs and startups on Rex.zone.

Workflows & Domains

You will operate inside end-to-end LLM training pipelines: dataset sourcing, preprocessing, annotation, QA checks, model training, offline/online evaluation, and RLHF-based reinforcement. Domains include NLP (NER, text classification, prompt evaluation), computer vision annotation (detection, segmentation), content safety labeling, and LLM evaluation. Core objectives emphasize training data quality, annotation guidelines compliance, model performance improvement, and large language model evaluation with measurable metrics.

Required Skills

Foundational STEM skills (statistics, linear algebra, algorithms), attention to detail, and guideline literacy are essential. Technical skills may include Python or SQL for data handling, familiarity with annotation tools (Label Studio, CVAT), NLP libraries (spaCy), and quality frameworks (inter-annotator agreement, rubric design). Experience with RLHF workflows, prompt engineering, error analysis, QA evaluation, and privacy/content safety practices is valuable. Senior roles expect process ownership and evaluation design.

Compensation & Engagement Models

Openings span entry-level to senior. Engagement types include remote contract, freelance, and full-time placements. Compensation varies by project complexity and domain: hourly, per-task, or milestone-based structures are common. Senior evaluators and QA leads may command higher rates for rubric development, audit leadership, and advanced LLM testing. All opportunities are listed and managed through Rex.zone with transparent scopes and deliverables.

Employers Hiring on Rex.zone

Rex.zone hosts roles from AI labs, tech startups, BPOs, and annotation vendors seeking vetted STEM talent for scalable AI training operations. Projects include data labeling sprints, long-term evaluation programs, content safety initiatives, and RLHF experiments. Global, time-zone-flexible teams deliver consistent quality aligned to standardized workflows and audit requirements.

How to Apply

Create your profile on Rex.zone, verify skills, and complete short assessments focused on annotation guidelines compliance and training data quality. Attach past work samples (NER projects, CV annotation sets, evaluation reports), specify domain preferences (NLP, computer vision, content safety, LLM training), and apply to remote, contract, freelance, or full-time roles. Shortlisted candidates may undergo trial tasks measuring accuracy, speed, and QA reliability.

Frequently Asked Questions

Q: What is a remote STEM job in AI/ML on Rex.zone?
It’s a science, technology, engineering, and mathematics role performed online, focused on AI/ML training operations: data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and RLHF-based LLM evaluation.
Q: Which workflows will I work on?
Typical workflows include training data quality reviews, annotation guidelines compliance checks, model performance improvement via error analysis, and large language model evaluation using task rubrics and RLHF feedback.
Q: Which domains are available?
Openings cover NLP (NER, text classification), computer vision (detection, segmentation), content safety, and LLM training pipelines including prompt evaluation and RLHF reward modeling.
Q: Are there roles for different seniority levels?
Yes. Opportunities span entry-level annotators and raters to senior QA leads, evaluation specialists, and RLHF coordinators responsible for rubric design and measurement strategies.
Q: What engagement types can I find?
Rex.zone lists remote, contract, freelance, and full-time positions across AI labs, tech startups, BPOs, and annotation vendors.
Q: Which tools might be used?
Teams commonly use Label Studio, CVAT, and internal evaluation dashboards. Familiarity with Python, spreadsheets, and QA checklists is helpful for data handling and audit tasks.
Q: Do I need prior ML experience to apply?
Entry-level roles emphasize guideline adherence and accuracy. Senior roles expect experience with evaluation design, RLHF processes, and analytical skills for model performance improvement.
Q: How do I apply on Rex.zone?
Create a profile, complete skill verifications, submit portfolio samples, and apply to listed projects. Some employers request trial tasks to assess accuracy, speed, and QA reliability.

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