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Appen
Labelbox
Mercor
OneForma
Scale AI
Text
Document
Image
Entity (NER) Classification
Classification
RLHF
Object DetectionOver the past few years, I have built solid hands-on experience in data labeling across text, image, and audio modalities, contributing to training datasets for natural language processing, computer vision, and conversational AI projects. My work has spanned sentiment annotation, named entity recognition, intent classification, and bounding box labeling, using platforms such as Scale AI, Labelbox, and Amazon Mechanical Turk. I consistently maintain high accuracy rates and have developed a strong eye for edge cases — ambiguous inputs that require careful judgment rather than mechanical tagging — which is where intermediate labelers often make the most impact. What sets me apart is my ability to apply contextual reasoning to nuanced annotation tasks, particularly in areas involving tone, subjectivity, or domain-specific language. I have contributed to quality assurance workflows, including reviewing and calibrating inter-annotator agreement, and I am comfortable working within detailed style guides and rubrics. I take instruction-following seriously and adapt quickly when guidelines are updated mid-project — a critical skill in fast-moving AI development cycles where dataset requirements evolve frequently.
TextEntity (NER) ClassificationTagged entities including names, organizations, dates, and locations across diverse document types. Worked within strict style guides and flagged annotation conflicts for QA review. Contributed to dataset consistency checks and participated in guideline update reviews, helping reduce labeling errors across the broader annotation team.
TextRLHFEvaluated and ranked AI-generated responses for quality, helpfulness, and factual accuracy as part of a reinforcement learning from human feedback (RLHF) pipeline. Applied structured rubrics to assess tone, coherence, and safety compliance. Demonstrated consistent judgment on ambiguous or sensitive prompts requiring nuanced reasoning beyond surface-level review.
TextClassificationAnnotated large volumes of customer-facing text data for sentiment polarity (positive, negative, neutral) and intent classification. Reviewed edge cases flagged by team leads and contributed to calibration sessions to improve inter-annotator agreement. Maintained accuracy above 95% throughout the project lifecycle while adhering to detailed annotation guidelines.
Bachelor of Science, Sociology
Project Manager
Marketing Team Leader
