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Scale AI
Data Annotation Tech
AWS SageMaker
Labelbox
Label Studio
Appen
Toloka
Text
Bounding Box
Segmentation
Classification
Point/Key Point
Text Generation
Question Answering
Text Summarization
RLHF
Evaluation/Rating
Data CollectionChirag Shetty Newcastle upon Tyne, UK [email protected] SUMMARY - Outlier | DataAnnotation | MTurk - Independent AI Data Annotator & Evaluator 01/2024 - 05/2026 Evaluated and ranked AI-generated responses based on accuracy, relevance, factuality, and instruction-following criteria. Performed text annotation, classification, categorization, and quality review tasks across multiple AI training projects. Conducted research and fact-checking to support high-quality data labeling and model evaluation. Applied detailed project guidelines and maintained consistency across large volumes of annotation work. Identified inaccuracies, hallucinations, policy violations, and edge cases in AI generated content. Contributed human feedback used to improve large language model performance and response quality. Worked independently in remote environments while meeting productivity and quality expectations. Completed data collection, validation, content assessment, and evaluation tasks across diverse domains.
TextClassificationCompleted large-scale annotation, categorization, validation, and content review tasks. Performed text classification, data verification, sentiment analysis, quality assurance, and research-based tasks supporting machine learning and AI training datasets.
TextEvaluation/RatingReviewed and evaluated AI-generated content across diverse domains. Conducted response assessment, quality scoring, factuality verification, prompt evaluation, and comparative analysis of model outputs. Applied detailed project guidelines to maintain consistency and annotation quality.
TextRLHFContributed to AI training and evaluation workflows through RLHF-based annotation tasks. Evaluated, ranked, and reviewed AI-generated responses for accuracy, factuality, instruction-following, relevance, reasoning quality, and safety. Performed response comparison, quality assessment, research, fact-checking, and guideline-based evaluation across diverse domains. Provided structured human feedback used to improve large language model performance and reliability.
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