Senior Data Labeler / AI Training Data Specialist (xAI Solutions)
Led a team to label high-quality instruction-response pairs for fine-tuning large language models and improved inter-annotator agreement to 96.8%. Created detailed annotation guidelines and rubrics for RLHF preference data to enhance model alignment and reduce toxic outputs. Conducted quality audits on large sample volumes and performed red-teaming by labeling adversarial prompts and harmful content for AI safety guardrails. • Labeled 450,000+ instruction-response pairs for fine-tuning. • Developed RLHF preference labeling guidelines and rubrics. • Audited 150,000+ samples to correct systematic labeling errors. • Labeled adversarial/harmful prompts for multilingual AI safety training.