Karun Vysyaraju
Senior Software Engineer at Amazon
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolKarun Vysyaraju is a Senior Software Engineer with seven years of experience building scalable ML-driven systems, currently on Amazon’s Supply Chain Optimization teams in Seattle. He holds a Master’s in ECE from Carnegie Mellon and focused his graduate research on deep learning, computer vision, and scalable Bayesian optimization as a CMU research assistant. At CMU he contributed significant backend improvements to the Dragonfly open-source library for Bayesian optimization, reflecting a strong applied research-to-production skillset. Prior to Amazon he spent 2.5 years in verification R&D at Synopsys, giving him a solid foundation in systems reliability and low-level engineering. He blends research rigor with production experience—designing ML and causal-inference pipelines to estimate long-term value in inventory systems. Colleagues describe him as someone who moves smoothly between algorithmic innovation and pragmatic engineering trade-offs.
7 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree Electronics and Communication Engineering, Bachelor’s Degree Electronics and Communication Engineering at Indian Institute of Technology, GuwahatiMaster's in Electrical and Computer Engineering, Master's in Electrical and Computer Engineering at Carnegie Mellon University
Hindi, Telugu, English
Github Skills (10)
algorithms10
machine-learning10
bayesian10
python10
optimisation10
optimization10
numpy9
web-framework9
scikit8
scikit-learn8
Programming languages (2)
Github contributions (5)
dragonfly/dragonfly
Aug 2018 - Mar 2019
An open source python library for scalable Bayesian optimisation.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits, 10 pushes, 1 branch in 6 months
Contributions summary:Karun primarily focused on integrating and modifying code for the Dragonfly library, enhancing its core functionality. Contributions included merging and refactoring Python modules (dragonfly.py, maximise_function.py, config_parser.py) related to Bayesian optimization. Further work involved adding and modifying functions for synthetic benchmarks, and updating the demo script to demonstrate core capabilities. The user also improved and extended features within the gp/euclidean_gp.py and mf_gp.py files, indicating a focus on improving the core Bayesian optimization algorithms.
python-librarybayesian-optimisationscalablepythondecomposition
karunraju/NFF
Jan 2019 - Jun 2019
Navigate, Fetch and Find
Contributions:1 PR, 2 pushes, 9 branches in 4 months
fetchjavascriptnavigate
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