Sunil Sudhakar
Staff Engineer at NVIDIA
Hyderabad, Telangana, India
Summary
🤩
Rockstar🎓
Top SchoolSunil Sudhakar is a Staff Engineer with 11 years of experience specializing in high-performance distributed systems, ML infrastructure, and emerging compute paradigms like quantum computing and LLMs. Based in Hyderabad, he has driven scalable networking and GPU-centric software at NVIDIA and Mellanox, bringing production-grade expertise in multi-node, multi-GPU testing and CI/CD automation. His open-source contributions to the JAX ecosystem demonstrate deep practical knowledge of SLURM, Docker, and test orchestration to validate GPU jaxlib nightly builds across clusters. Sunil combines systems-level engineering with ML tooling fluency, enabling reliable large-scale model workflows and parallel compute stacks. He holds a Master's in Computer Science from Louisiana Tech and often bridges research ideas with production realities.
11 years of coding experience
13 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Louisiana Tech UniversityKendriya Vidyalaya SangathanSSCET
Github Skills (12)
machine-learning10
jax10
pytest10
slurm10
python10
cicd10
dockers9
testing9
docker9
gcp7
kubernetes-pods5
kubernetes5
Programming languages (6)
Github contributions (5)
jax-ml/jax
Aug 2022 - Oct 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:25 reviews, 9 commits, 15 PRs in 2 months
Contributions summary:Sunil primarily focused on enhancing the CI/CD pipeline and integrating multi-node, multi-GPU testing for JAX. Their contributions include adding and modifying SLURM job scripts, Docker configurations, and test files to support automated testing across multiple nodes. The user also implemented changes to test GPU jaxlib nightly builds, demonstrating a strong understanding of continuous integration and distributed machine learning environments. These changes help improve the reliability and scalability of JAX.
pytorchpythonjitautomatic-differentiationgpu
sudhakarsingh27/jax
Jul 2022 - Nov 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Contributions:308 pushes, 45 branches in 3 months
pytorchpythonjitgpunumpy
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