Abhishek Jindal
Software Engineer at Microsoft
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAbhishek Jindal is a pragmatic machine learning and software engineer with 8 years of experience building production-ready AI systems across industry and research, currently focused on AI Frameworks and Platform work at Microsoft. He combines deep learning and NLP expertise from graduate research at UC Irvine—where he developed transfer-learning solutions for emotion detection and a novel end-to-end approach for missing data—with hands-on engineering skills in Python, C++ and multi-GPU inference pipelines. Prior roles at HPE Labs, Rakuten, Two Roads Tech and Opera Solutions show a track record of translating research into impactful products, from 30x faster video-frame models to HFT trading strategies that improved P&L. An active open-source contributor, he helped integrate PyTorch-style eager execution into the high-profile ONNX Runtime, including CI and Windows build fixes. Based in Sunnyvale, he pairs strong quantitative instincts with practical deployment experience and a competitive streak evidenced by Kaggle Competitions Expert standing.
8 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology KanpurUniversity of California, Irvine
English
Github Skills (9)
pytorch10
machine-learning10
python10
cicd9
devops9
onnx9
build-automation8
hardware-acceleration7
deep-learning7
Programming languages (2)
Github contributions (5)
microsoft/onnxruntime
Aug 2021 - Apr 2022
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Role in this project:
ML Engineer & DevOps Engineer
ML Engineer & DevOps EngineerContributions:66 reviews, 78 commits, 64 PRs in 7 months
Contributions summary:Abhishek primarily contributed to the implementation and maintenance of the eager mode pipeline within the ONNX Runtime project, focusing on integration with PyTorch. Their work involved modifying build scripts, environment settings, and dependency installations. The user also addressed issues related to Windows builds and CI/CD pipelines. This included fixing warnings and errors related to the eager mode, and incorporating and testing the setup on Windows for multiple python versions.
runtimetrainingtensorflowai-frameworkaccelerator
ajindal1/DeepSpeed
May 2023 - Sep 2023
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Contributions:12 pushes, 5 branches in 4 months
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