Rishi Thakkar
Senior Deep Learning Software Engineer On TensorRT at NVIDIA
Urbana-Champaign Area United States
Summary
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Rockstar🎓
Top SchoolRishi Thakkar is a Senior Deep Learning Software Engineer at NVIDIA with 10 years of experience specializing in accelerating inference on GPUs using CUDA, optimization techniques, and deep knowledge of GPU architecture through work on TensorRT. He blends software and hardware expertise acquired from ML implementation and profiling on GPUs and FPGAs at NCSA with hands-on systems and FPGA teaching roles at UIUC, giving him a rare full-stack perspective from silicon to production AI services. Rishi has built cloud-ready back-end systems and data pipelines earlier in his career, and he regularly applies that systems-thinking to squeeze latency and efficiency gains in real-world AI deployments—from cloud to self-driving applications. Colleagues describe him as organized, tenacious, and driven by practical problem-solving, with a longstanding interest in using engineering to make measurable impact.
10 years of coding experience
1 year of employment as a software developer
Bachelor of Science (BS), Computer Engineering, Bachelor of Science (BS), Computer Engineering at University of Illinois Urbana-Champaign
English, Spanish, Hindi
Github Skills (70)
runtimes10
nvidia10
cuda10
pytorch10
opencl9
emacs9
python9
vim9
jenkins-plugin9
cpp9
llm9
large-language-models9
inference9
amdgpu9
tensorrt9
Programming languages (10)
Github contributions (5)
atrifex/CNN-Acceleration
Mar 2017 - Apr 2017
Implementing CNN code in CUDA and OpenCL to evaluate its performance on NVIDIA GPUs, AMD GPUs, and an FPGA platform.
Contributions:58 commits, 54 pushes, 1 branch in 1 month
amdgpucudafpganvidiaopencl
Atrifex/ECE-438
Mar 2017 - Apr 2017
Contributions:231 commits in 1 month
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