David Nam
Software Engineer at Intel
South Korea
Summary
🤩
Rockstar🎓
Top SchoolDavid Nam is a software engineer with five years of experience focused on CI/CD systems and low-level performance work, currently contributing to Intel and living in South Korea. He builds and operates continuous integration pipelines with TeamCity and Jenkins while integrating tools like Teamforge, Klocwork, CMake and scripting languages to keep large codebases ship-ready. On open-source, he has contributed to the high-profile OpenVINO toolkit, adding new deep-learning operations and improving template plugin tests and GPU memory/layout performance. His background in electronics (BEng) and a master’s in computer engineering give him a strong systems-oriented perspective that informs both backend and ML-engineering tasks. Beyond engineering, he spent over a decade in community leadership as an assistant pastor, suggesting strong communication and mentoring skills in cross-functional teams. He combines practical CI expertise with hands-on optimization of ML inference code, bridging infrastructure and performance-sensitive development.
5 years of coding experience
석사, 컴퓨터공학, 석사, 컴퓨터공학 at 아주대학교
Github Skills (11)
testing10
machine-learning10
c-language10
inference10
deep-learning10
cprogramming-language10
performance-optimization10
ai10
openvino10
computer-vision9
tensorflow3
Programming languages (1)
Github contributions (3)
openvinotoolkit/openvino
May 2021 - Jun 2022
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:73 reviews, 15 commits, 86 PRs in 1 year 1 month
Contributions summary:David contributed to the OpenVINO™ toolkit by adding new operations and tests for deep learning models, specifically focusing on implementing and testing the `atan` and `ceiling` operations. They also migrated several arithmetic operations such as `divide`, `round`, `range`, `fake_quantize`, `reshape`, `pooling`, `matmul`, and `idft` to template plugin tests, indicating a focus on improving the testing infrastructure. Additionally, the user worked on performance optimizations by addressing issues related to data layout and memory handling within the GPU implementation, demonstrating a focus on low-level code and potential performance bottlenecks.
aiinferenceopenvinodeep-learningcomputer-vision
davidsnam-intel/openvino
May 2021 - Jul 2026
OpenVINO™ Toolkit repository
Contributions:526 pushes, 173 branches in 5 years 2 months
pytorchdeep-learninggpuopenvino-toolkitcomputer-vision
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