Nishanth Hegde
Software Engineer II at Microsoft
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolNishanth Hegde is a Software Engineer II with a decade of experience building production-grade ML and cloud systems, currently applying LLMs to security problems at Microsoft in Seattle. He has a strong AWS and Amazon background, having implemented anomaly detection at CloudWatch and production services at Amazon prior to his current role. His roots in research and open source—contributing rigorous tests and refactors to the popular reinforcement learning toolkit garage—reflect a focus on reliability and reproducibility in ML systems. With an MS in Computer Science from USC and hands-on experience across C++, TensorFlow, and test automation, he bridges research-grade algorithms and production engineering. Notably, he has repeatedly operated at the intersection of security, ML, and systems, bringing both academic rigor and enterprise-scale delivery to complex problems.
9 years of coding experience
7 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at University of Southern CaliforniaBachelor of Technology Computer Science and Engineering, Bachelor of Technology Computer Science and Engineering at PES University
Stackoverflow
1reputation
0reached
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Github Skills (9)
unit-testing10
pytorch10
python10
testing10
tensorflow9
repr9
rep9
algorithms8
algorithm8
Programming languages (2)
Github contributions (5)
rlworkgroup/garage
May 2019 - Nov 2019
A toolkit for reproducible reinforcement learning research.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:13 commits, 28 PRs, 70 pushes in 5 months
Contributions summary:Nishanth primarily focused on ensuring the correctness of the `DeterministicMLPPolicyWithModel` through comprehensive testing. They added a suite of unit tests to verify API conformance and output consistency between the refactored policy and the original `DeterministicMLPPolicy`. The user's contributions involved creating and modifying test files to validate different aspects of the policy's behavior, especially during the transition of using `DeterministicMLPPolicy` to `DeterministicMLPPolicyWithModel`.
pytorchreinforcement-learningreproduciblereproducibilityrl-algorithms
nish21/mlpack
Feb 2017 - Jan 2018
Contributions:18 pushes, 4 branches in 11 months
scalabledeep-learningmachine-learningmachine-learning-librarylibrary-learning
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