Vivek Panyam
Senior Staff Machine Learning Engineer at Cruise
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolVivek Panyam is a Senior Staff Machine Learning Engineer with 13 years of experience building production-grade deep learning systems, currently leading autonomy ML efforts at Cruise in the San Francisco Bay Area. He co-created Neuropod, an open-source library for running models across frameworks that powered hundreds of models at Uber ATG and earned a GTC presentation invite from NVIDIA—evidence of his focus on bridging research and production. His background spans technical leadership roles across Uber, Google, and Facebook, where he shipped perception models for self-driving vehicles and drove integrations of multiple DL frameworks. Beyond engineering, he has played a heavy role in hiring and defining technical interview processes, and he pairs hands-on C++/backend work with product-minded infrastructure design. An early entrepreneur, he also built Lagom, a minimalist homepage that scaled to millions of monthly pageviews, showing a knack for shipping polished user-facing projects as well as backend systems.
13 years of coding experience
7 years of employment as a software developer
Oakland University Summer Mathematics InstituteCulture and Leadership, Culture and Leadership at Harvard Business School Executive EducationNovi High SchoolBSE, Computer Science and Computer Engineering, BSE, Computer Science and Computer Engineering at University of Pennsylvania
English, Telugu
Github Skills (10)
data-serialization10
c-language10
cprogramming-language10
serialization10
tensorflow9
tensorrt9
tensor9
python-multiprocessing8
multi-process8
multiprocessing8
Programming languages (8)
Github contributions (5)
uber/neuropod
Nov 2018 - Sep 2022
A uniform interface to run deep learning models from multiple frameworks
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 releases, 66 reviews, 467 commits in 3 years 11 months
Contributions summary:Vivek primarily worked on improving the C++ interface and underlying data structures of the Neuropod library. They refactored and added features to the serialization and de-serialization process, contributing to the foundation for out-of-process execution. The user also implemented tensor-related features to improve performance and usability. Their work focused on enhancing the core functionality of the Neuropod framework, as evidenced by the changes to serialization and string tensor handling.
pytorchmxnetdeep-learningmachinelearninginference
Infinity/Iris
Feb 2015 - Sep 2017
Contributions:4 commits, 1 push in 2 years 7 months
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