Data Labeler
AI training initiative focused on improving Large Language Models. It involves human workers performing pairwise comparisons and data labeling across text, images, and video to ensure AI responses are accurate, safe, and helpful.
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I am a detail-oriented cybersecurity student with hands-on experience in information security and IT support, bringing a strong foundation in data analysis, technical troubleshooting, and process optimization. My background includes working with large sets of network and security data, performing vulnerability assessments, and monitoring network traffic using tools like Wireshark and tcpdump. I am skilled in documenting findings, identifying anomalies, and ensuring data accuracy, all of which are critical for high-quality data labeling and annotation. With proficiency in Python, technical documentation, and a keen eye for detail, I am eager to contribute to AI training data projects, ensuring precise and reliable data preparation for machine learning models.
AI training initiative focused on improving Large Language Models. It involves human workers performing pairwise comparisons and data labeling across text, images, and video to ensure AI responses are accurate, safe, and helpful.
Scope & Tasks: Contributed to a large-scale multimodal AI training project focusing on Reinforcement Learning from Human Feedback (RLHF). Performed pairwise comparisons to rank model responses and conducted data labeling for text, image, and video content to improve model accuracy and safety. Project Size & Quality: Worked as part of a high-volume global team, adhering to strict Quality Assurance (QA) protocols. Measures included regular accuracy audits, "gold set" validations, and strict compliance with project-specific rubrics to ensure data integrity and model alignment.
Bachelor of Science, Information Systems
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