Machine Learning Training Data Analyst
The AI training data project I contributed to at Outlier involved large-scale supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) for multiple LLM systems serving major technology companies. My specific data labeling tasks included creating high-quality prompt-response pairs across technical, creative, and multilingual domains, evaluating AI-generated content for accuracy and human alignment, and conducting comprehensive response ranking to optimize reward model training. The project encompassed thousands of training examples over an 18-month period, with rigorous quality measures including 95%+ accuracy standards for factual verification, consistent inter-annotator agreement protocols, and strict adherence to safety and cultural appropriateness guidelines across English, French, and Arabic language outputs, directly contributing to the development of production-ready conversational AI systems used by millions of users worldwide.