Job Description
Note: The job is a remote job and is open to candidates in USA. Rex.zone is a company focused on AI/ML engineering workflows, and they are seeking an Entry Level STEM professional to support LLM training pipelines through data labeling and evaluation. The role involves executing data labeling, performing QA evaluations, and collaborating with engineering partners to enhance training data quality.
Responsibilities
- Execute data labeling across text, image, and multimodal datasets
- Perform QA evaluation using sampling plans, disagreement analysis, and error taxonomies
- Complete RLHF preference ranking and prompt evaluation aligned to rubrics
- Annotate NLP tasks (e.g., named entity recognition, intent classification) with consistent schemas
- Support computer vision annotation (bounding boxes, segmentation) as needed
- Apply content safety labeling policies for harmful or sensitive content
- Document edge cases and provide rationales to improve guidelines and training data quality
- Collaborate asynchronously with distributed engineering and ops partners
Skills
- US-based and available for remote, full-time work
- STEM background (degree, bootcamp, or equivalent experience) and quantitative reasoning
- Ability to learn and follow detailed annotation guidelines
- Strong written communication and attention to detail
- Familiarity with spreadsheets and basic Python or SQL for lightweight data checks
- Experience with LLM evaluation, RLHF, or prompt evaluation
- Exposure to NLP and/or computer vision annotation tools
- Understanding of content safety labeling and policy interpretation
- Experience with training data quality metrics or inter-annotator agreement
Company Overview
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