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Principal ML scientist scaling Reinforcement Learning@ Amazon AGI, Previously: Uber AI Labs, PhD@UT Austin
Building scalable RL algorithms for training foundational models, as part of the Artificial General Intelligence team at Amazon. Developed Titan LLMs for AWS Bedrock -- pre-training/fine-tuning/learning-from-human-feedback. Previously worked on Neural Architecture Search, Meta-learning, Network compression, Multi-Agent and Open-ended Reinforcement Learning. I have enjoyed working in diverse teams, building prototype solutions for challenging research problems and publishing in top conferences. Previously, during my PhD thesis, I developed novel meta-learning techniques for training recurrent neural networks.
The University of Texas at Austin
Doctor of Philosophy - PhD, Computer Science
January 1, 2012 – January 1, 2018
The University of Texas at Austin
Masters, Computer Science
January 1, 2009 – January 1, 2012
Netaji Subhas Institute of Technology
Bachelors of Engg., Electronics and Communications
N/A – Present
Amazon AGI Foundations
Principal Machine Learning Scientist
January 1, 2026 – Present
Amazon
Senior ML scientist@ Artificial General Intelligence
January 1, 2024 – December 1, 2025
Amazon Web Services (AWS) AI Labs
Senior Machine Learning Scientist
June 1, 2020 – January 1, 2024
San Francisco Bay Area
Uber AI Labs
Research Scientist
May 1, 2018 – June 1, 2020
San Francisco Bay Area
Sentient Technologies
Research Scientist
July 1, 2016 – May 1, 2018
San Francisco Bay Area
Department of Computer Science, The University of Texas at Austin
Teaching Assistant, Artificial Intelligence
August 1, 2012 – August 1, 2015
Department of Computer Science, The University of Texas at Austin
Graduate Research Assistant
August 1, 2009 – July 1, 2012
Texas instruments
Senior Design Engineer
January 1, 2004 – January 1, 2009
Bengaluru, Karnataka, India
Cultural Fit Analysis
The candidate has worked at several prominent tech companies (Amazon, Uber AI Labs, Sentient Technologies) and has a strong academic background, indicating a drive for innovation and excellence. The diversity of projects, from multimodal foundational models to AI music composers, suggests adaptability and a broad interest in AI applications. This aligns well with a culture that values cutting-edge research and practical implementation.
Soft Skills & Operational Fit
The candidate's resume indicates a strong background in research and development within large organizations, suggesting an ability to work in structured environments and contribute to complex projects. The roles as a Teaching Assistant and Graduate Research Assistant also imply communication and collaboration skills. However, specific soft skill assessments are not available.