Summary
Currently enhancing AI model alignment and performance for Outlier AI by delivering critical Reinforcement Learning from Human Feedback (RLHF) and maintaining high accuracy in evaluation tasks.
Highlights
Delivered over 1,000 Reinforcement Learning from Human Feedback (RLHF) evaluations on AI prompts, significantly enhancing model alignment and performance.
Maintained a 98% accuracy rate in complex AI evaluation tasks, consistently exceeding aggressive weekly targets and ensuring high-quality model training data.
Conducted advanced prompt-tuning annotation experiments utilizing proprietary internal tooling to refine AI model responses and optimize user interactions.
Collaborated asynchronously with international teams across diverse time zones, leveraging Notion, Slack, and custom dashboards to streamline project workflows.