Large Language Model (LLM) Trainer/Rater
Trained and fine-tuned Large Language Models (LLMs) by rating, generating, and reviewing text-based data to improve model coherence, relevance, and safety.
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I am an experienced AI data annotator and quality assurance specialist with a strong background in creating high-quality training data for diverse domains, including autonomous vehicles, blockchain analysis, large language models, and semantic labeling. My work spans projects with organizations like Sama AI, OneForma, Crowdgen, and OpenTrain AI, where I have honed my skills in data labeling, LiDAR annotation, search evaluation, and QA. I am adept at handling ambiguous guidelines and imperfect data, ensuring accuracy and consistency through meticulous attention to detail. My technical expertise includes Python, web scraping, version control with Git, and data entry, complemented by an understanding of the end-to-end AI lifecycle. I am passionate about transforming raw data into actionable insights and continuously learning new tools and methods to improve data quality and model outcomes.
Trained and fine-tuned Large Language Models (LLMs) by rating, generating, and reviewing text-based data to improve model coherence, relevance, and safety.
Performed detailed semantic annotation on various data types, ensuring accurate labeling of content elements to enhance machine learning model understanding and performance.
Precisely labeled and segmented image and sensor data (e.g., LiDAR, camera) for training Machine Learning models critical for autonomous driving systems.
Assessed the relevance, accuracy, and quality of search engine results, providing crucial feedback to optimize search algorithms and improve user experience.
Conducted diverse data annotation tasks, including bounding box creation, text categorization, and image tagging, to support the development of AI/ML applications.
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