Building the frontier of Large Language Models & Post-Training
Advancing state-of-the-art AI through novel post-training techniques — instruction tuning, preference optimization, and scalable alignment methods that shape how hundreds of millions of people interact with AI.
I am a Senior Research Engineer at [Company] specializing in large language models and post-training techniques. My work focuses on developing novel methods that improve the capabilities, reliability, and alignment of production-scale language models.
My research spans instruction tuning, reinforcement learning from human feedback (RLHF), preference optimization, and scalable evaluation frameworks. The techniques I have developed are integrated into production LLM systems used by hundreds of millions of people worldwide — making direct impact on how AI assists in education, scientific discovery, medicine, and everyday productivity.
I received my [Degree] from [Institution] in machine learning and natural language processing. My work has been published at top-tier venues including NeurIPS, ICML, ACL, and ICLR, and recognized by leading technology publications covering the AI industry.
My contributions advance the United States' position at the frontier of artificial intelligence — a domain recognized as critical to national competitiveness, scientific progress, and economic growth.
Coverage of my research and expert perspectives in leading technology publications.
Open to research collaborations, speaking invitations, and expert consultations in large language models and AI post-training.