Sandeep Narayana is an experienced engineer in the San Francisco Bay Area with 11 years building machine and deep learning systems, computer vision pipelines, and graph compilers at Intel. He has driven backend and MLOps work on high-profile open-source projects like nGraph—implementing CPU backprop, Relu/Sigmoid ops and serializer support—and helped enable hybrid backends for efficient model deployment. At Intel he's contributed across Atom/FPGA/iGPU stacks, Nervana NNP training, and next-gen Movidius VPU architecture, blending low-level optimization with production ML tooling. His early research applied neural approaches to proactive thermal-aware scheduling and deeper CNNs for video classification, reflecting a habit of turning research insights into practical system improvements. Colleagues would note his uncommon mix of compiler-level intuition and hands-on deployment experience that speeds AI workloads from prototype to product.
nGraph - open source C++ library, compiler and runtime for Deep Learning
Role in this project:
Back-end Developer & MLOps Engineer
Contributions:70 commits, 11 PRs, 141 pushes in 1 year 1 month
Contributions summary:Sandeep primarily focused on enhancing the nGraph backend for deep learning. Their contributions included implementing Relu operations, serializer support, and backpropagation for the CPU. They also worked on integrating the Sigmoid function into the core fusion process, improving the framework's capabilities. Moreover, they contributed to hybrid backend, which indicates efforts related to deployment and optimization of the models.
Contributions:24 pushes, 1 branch in 4 years 3 months
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