Rajan Singh
ML Infra at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRajan Singh is an ML infrastructure engineer with eight years of experience building scalable training and inference systems for large models, currently focused on distributed solutions at NVIDIA. He has a strong background in low-level performance work—developing custom CUDA kernels and contributing framework-level optimizations for PyTorch and TensorFlow—and a proven track record delivering production ML infra at AWS and Adobe. Rajan’s prior work includes model-parallel training, ONNX integration, and pushing optimizations into core repos, reflecting both systems depth and cross-team collaboration. Based in San Jose, he blends graphics/driver development roots with modern deep learning tooling, making him adept at bridging hardware-aware performance engineering and developer-facing ML platforms.
8 years of coding experience
18 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Manipal Institute of TechnologySelf Driving Car NanoDegree Term 1 Computer Vision & Deep Learning, Self Driving Car NanoDegree Term 1 Computer Vision & Deep Learning at UdacityMS Computer and Information Sciences, MS Computer and Information Sciences at University of Florida
Github Skills (57)
portable10
pytorch10
javascript10
docker10
dep10
deploying10
interoperability10
dataflow10
python10
data-science10
mxnet10
machine-learning10
onnx10
scheduler10
sagemaker10
Programming languages (5)
Github contributions (5)
rajanksin/pytorch
Sep 2019 - May 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:4 PRs, 8 pushes, 1 branch in 3 years 8 months
pythongpu-accelerationdeep-learninggpuacceleration
spidyDev/incubator-mxnet
Mar 2018 - Jun 2018
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:1 PR, 62 pushes, 13 branches in 3 months
pythonschedulerdataflowmutationorchestration
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