Jithun Nair
MTS Design Engineer at AMD
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJithun Nair is an experienced design and verification engineer with 8+ years blending silicon-focused physical design and block-level functional verification, currently working as an MTS Design Engineer at AMD in Palo Alto. He has a strong background in full-chip and block physical design from roles at Texas Instruments and Qualcomm, and holds an M.S. in Electrical Engineering from Stanford and a B.Tech from IIT Kharagpur. Beyond hardware, he contributes to high-profile open-source infrastructure for PyTorch, improving CI/CD, ROCm support, and site reliability—helping ensure GPU-accelerated deep learning builds and documentation stay reliable. That cross-domain fluency, from low-level silicon verification to DevOps for one of the largest ML frameworks, is a distinguishing strength that enables him to bridge system-level performance needs with production deployment reliability.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering, Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering at Indian Institute of Technology, KharagpurMaster of Science (M.S.), Electrical Engineering, Master of Science (M.S.), Electrical Engineering at Stanford University
Github Skills (19)
github-ci10
docker10
website-development10
python10
bash10
dockers10
cicd10
script10
roc10
build-automation10
cuda10
githubaction-workflow10
javascript9
cmake9
gpu9
Programming languages (16)
Github contributions (5)
pytorch/pytorch
Oct 2018 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
DevOps Engineer & Build & Release Engineer
DevOps Engineer & Build & Release EngineerContributions:351 reviews, 73 commits, 444 PRs in 4 years 3 months
Contributions summary:Jithun primarily contributed to the continuous integration and continuous deployment (CI/CD) infrastructure for the PyTorch repository. They made several modifications to the build and docker scripts, specifically focusing on supporting ROCm (AMD's platform for GPU computing). Their work included updating docker image tags, integrating ROCm versions, and enabling features like Triton builds within the CI environment. They also addressed build failures and conflicts, improving the reliability of the CI/CD pipeline.
gpu-accelerationneural-networkpythonautogradgpu
pytorch/builder
Jan 2022 - Jan 2023
Continuous builder and binary build scripts for pytorch
Role in this project:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:22 reviews, 8 commits, 38 PRs in 1 year
Contributions summary:Jithun primarily focused on building and maintaining the CI/CD pipelines and build scripts for the PyTorch builder repository, specifically targeting ROCm. They implemented and updated Docker builds, automated wheel creation, and integrated with S3 for hosting builds. The user also made significant changes to the build scripts to support different ROCm versions and address dependencies, including modifications to install and configure MIOpen.
build-scriptpytorch
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