Sunil Srinivasa
Principal Software Engineer - AI at NVIDIA
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSunil Srinivasa is a Principal Software Engineer - AI with a Ph.D. and over a decade of industry experience building production ML systems at scale, now at NVIDIA after leading personalization and queryless shopping ML at Google. He bridges deep research and engineering—authoring cited publications and patents while shipping open-source projects like ai-economist and WarpDrive that have attracted significant community attention. His expertise spans LLMs, distributed multi-agent reinforcement learning, personalization, sequential decision-making, and counterfactual offline learning, with a track record of deploying systems used by hundreds of millions to billions of users. Notably, he has engineered ultra-fast distributed RL training frameworks and collaborated across academia and industry (MILA, PyTorch Lightning, NVIDIA) to accelerate multi-agent research-to-production pathways.
11 years of coding experience
20 years of employment as a software developer
Indian Institute of Technology MadrasPh.D Electrical Engineering, Ph.D Electrical Engineering at University of Notre Dame
Github Skills (4)
python10
deep-reinforcement-learning8
machine-learning8
documentation7
Programming languages (2)
Github contributions (5)
salesforce/ai-economist
Jul 2020 - May 2022
Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 reviews, 97 commits, 32 PRs in 1 year 10 months
Contributions summary:Sunil primarily addressed typos and made minor code cleanup adjustments across multiple Python files within the `ai_economist` repository, specifically the foundation components. They also contributed to updating the documentation, including colab links and dependency descriptions. Moreover, the user refactored the training script and integrated necessary code for running the project on the GPU with WarpDrive.
behaviorsfairness-mlsimulation-frameworkmahjongdeep-reinforcement-learning
MetaMind/ray-internal
Jun 2020 - Dec 2020
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:13 PRs, 49 pushes, 19 branches in 5 months
scalableraydistributed-applicationshyperparametersimple-framework
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.