Venkataramana Ganesh
Senior AI Engineer at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVenkataramana Ganesh is a Senior AI Engineer with nine years of experience building high-performance ML and inference systems, currently focused on scaling TensorRT-LLM at NVIDIA in San Jose. He blends deep GPU engineering—contributing to CuPy and RAPIDS cuML with work like porting LOBPCG and accelerating Random Forests—with research-grade ML from a Georgia Tech MS, including NeurIPS/ICML collaborations on transformers and GNNs. Known for shipping developer tooling as well as kernels, he built an ONNX linting and GUI editor integrated into Nsight DL Designer and routinely solves production inference bottlenecks. His open-source footprint shows both low-level algorithmic work (B-orthonormalization, eigensolvers) and pragmatic engineering (profiling NVTX, refactors) that drive measurable speedups.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Technology - BTech. (Hons.), Computer Science, Bachelor of Technology - BTech. (Hons.), Computer Science at National Institute of Technology, Tiruchirappallimaharishi vidya mandirarsha vidya mandirMasters, Computer Science, Masters, Computer Science at Georgia Institute of TechnologyCertificate, Quantum Computing, Certificate, Quantum Computing at Frontier Technology Institute
Stackoverflow
135reputation
10kreached
1answer
8questions
Github Skills (27)
deco10
c-language10
python10
decomposition10
sparse-matrix10
machine-learning10
machine-learning-algorithms10
nvidia10
gpu10
cupy10
cuda10
decompose10
cprogramming-language10
random-forest10
linear-algebra10
Programming languages (5)
Github contributions (5)
rapidsai/cuml
Sep 2020 - Mar 2022
cuML - RAPIDS Machine Learning Library
Role in this project:
ML Engineer
ML EngineerContributions:92 reviews, 26 commits, 40 PRs in 1 year 6 months
Contributions summary:Venkataramana primarily contributed to the cuML library by fixing bugs, refactoring code, and adding NVTX markers for profiling. They addressed issues related to NVTX marker color generation and test target changes. Furthermore, the user made significant changes to the metric functions and the parameters used in the RF models, including fixing a critical bug causing the tests to fail. They also focused on refactoring and cleaning up the Random Forest parameter initialization and documentation.
cudacumlnvidiadata-sciencegpu
cupy/cupy
Nov 2020 - Mar 2021
NumPy & SciPy for GPU
Role in this project:Back-end Developer & Algorithm Implementer
Back-end Developer & Algorithm ImplementerContributions:39 reviews, 90 commits, 2 PRs in 3 months
Contributions summary:Venkataramana's initial commit focused on implementing interfaces and tests, and modifying the \_\_init__.py file. Subsequent commits involve significant changes to the code, particularly within the cupyx/scipy/sparse/linalg/interface.py file, indicating a focus on the development of a linear algebra library. The user's work appears to be centered around the creation of a functional and efficient method for B-orthonormalizing vectors. Furthermore, the user contributed to the implementation of the LOBPCG solver algorithm.
cudapythoncusolvergpunumpy
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