Abhishek Jindal
Software Engineer at Microsoft
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAbhishek Jindal is a software engineer with 7 years of experience focused on AI frameworks and production ML, currently on the AI Frameworks/Platform team at Microsoft. He completed graduate research at UC Irvine under Prof. Padhraic Smyth, specializing in deep learning and NLP—developing transfer-learning approaches for emotion detection and a novel end-to-end deep learning method for datasets with missing values. In industry he has shipped high-impact solutions across Hewlett Packard Labs, HFT, and consulting—building a 30x faster multi-GPU inference pipeline, applying reinforcement learning to real-world problems, and improving trading strategy gains by over 20%. He is an active open-source contributor to the high-profile microsoft/onnxruntime project, leading integration work on eager-mode PyTorch support and stabilizing Windows CI/builds. A hands-on engineer with a 3.96 MS from UC Irvine and an IIT Kanpur B.Tech, he’s a Kaggle Competitions Expert who regularly reads SOTA papers to bring research into production.
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
devops9
onnx9
ci-cd9
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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