Summary
Shobha Vasudevan is a Principal Scientist at Amazon AGI with 11+ years driving research-to-production transitions for large-scale ML and generative models, focusing on inference-time scaling, RL pipelines, curriculum training and reward modeling. Previously a key contributor to Gemini post-training and optimization at Google/DeepMind, she has deep expertise across the full ML stack—transformers, GNNs, compilers (XLA), TPU architecture, and datacenter workload reliability. A former tenured professor at UIUC and visiting faculty at Stanford, she blends rigorous academic research with hands-on systems and product delivery, having led projects that improved TPU design, prevented hardware failures, and built LLM-based code and database models. Her work is recognized by multiple best paper awards and prestigious honors (NSF CAREER, ACM/IEEE awards), and she actively shapes the community through editorial roles and standards and by mentoring women in computing. An uncommon strength is her ability to optimize across layers—from algorithm and model design down to compiler and hardware validation—making her a rare bridge between theory and production-grade ML infrastructure.
10 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Engineering, Doctor of Philosophy (PhD) Computer Engineering at The University of Texas at Austin
M.S. Computer Engineering, M.S. Computer Engineering at Cockrell School of Engineering, The University of Texas at Austin
Bachelor of Engineering - BE Computer Engineering, Bachelor of Engineering - BE Computer Engineering at University of Mumbai