The quick read
Join Rex.zone as a remote Online Generalist. This role involves supporting AI/ML data operations, including data labeling and content safety evaluation. No degree necessary and flexible hours help make this an excellent opportunity for those seeking a low-stress position in technology. Experience with data can be an asset, but not required. Competitive pay offered.
Full job description
**Online Generalist Jobs In India (Remote, Full\-Time) — Role Overview**
Rex.zone is hiring remote, full\-time Online Generalists to support AI/ML data operations across LLM training pipelines. You will perform data labeling, RLHF evaluation, prompt evaluation, QA evaluation, and content safety labeling while following documented rubrics and annotation guidelines to improve training data quality and reduce model errors.
**What You Will Do**
* Label and review text and image data to support LLM training pipelines
* Complete RLHF preference ranking and prompt\-response evaluation using provided guidelines
* Perform QA evaluation for accuracy, consistency, and guideline adherence
* Apply content safety labeling and policy\-based ratings for unsafe content detection
* Resolve edge cases, document decisions, and escalate issues through defined workflows
**Core Workstreams You May Support**
* NLP annotation (classification, summarization checks, named entity recognition)
* LLM evaluation (response ranking, prompt evaluation, RLHF evaluation)
* Computer vision annotation (bounding boxes, segmentation, attribute tagging)
* QA operations (sampling plans, error taxonomies, reviewer calibration)
**Required Qualifications**
* Ability to follow detailed instructions and apply consistent judgment in a remote environment
* Strong written English and careful reading; able to explain labeling decisions clearly
* Comfort with web\-based tools, spreadsheets, and task queues
**Preferred Qualifications**
* Experience with data labeling, RLHF, prompt evaluation, content moderation, or AI evaluation programs
* Familiarity with NLP, named entity recognition, or computer vision annotation
* Experience improving training data quality via error analysis, calibration, or guideline refinement
**How Success Is Measured**
Accuracy, consistency, inter\-annotator agreement, QA pass rates, turnaround time, and adherence to annotation guidelines. Strong contributors help drive model performance improvement by identifying failure modes and suggesting rubric improvements.
**Compensation**
Competitive hourly pay: $30–$50/hr (USD), based on skills and project needs.
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