Rohan Jain
Hardware Engineer at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRohan Jain is a hardware engineer based in Mountain View with 10 years of hands-on experience designing and analyzing high-performance compute systems, from ASIC/SoC architectures to FPGA and AI accelerators. Currently at Google working on Pixel system architecture and ML power/performance analysis, he blends hardware microarchitecture expertise with software-aware co-design and hardware emulation. His open-source contributions span prominent ML projects including TensorBoard, TensorFlow, and XLA—demonstrating full-stack involvement from visualization UI fixes to compiler shape inference and test automation. Rohan’s background in embedded systems, RISC-V simulation, power electronics for high-voltage propulsion, and a business minor gives him a rare mix of practical hardware systems engineering, product-minded thinking, and sustainability-focused curiosity. He gravitates toward lean, cross-functional teams building cutting-edge, energy-conscious technologies and explores emerging domains like in-memory compute, neuromorphic and photonic approaches.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Science, Electrical and Computer Engineering, Bachelor of Science, Electrical and Computer Engineering at The University of Texas at AustinRidge High School
English, Hindi, Spanish
Stackoverflow
43reputation
1kreached
1answer
1question
Github Skills (35)
unit-testing10
simplify10
c-language10
visualization10
python10
testing10
typescript10
machine-learning10
inference10
deeplearning-ai10
deep-learning10
tensorflow10
typescript-types10
compiler10
xla10
Programming languages (5)
Github contributions (5)
tensorflow/estimator
Oct 2018 - Aug 2019
TensorFlow Estimator
Role in this project:
ML Engineer
ML EngineerContributions:12 commits in 10 months
Contributions summary:Rohan primarily contributed to the TensorFlow Estimator project by adding and testing canned estimators, specifically focusing on integrating V1 and V2 feature columns within the existing framework. Their work involved modifying test files to include comprehensive integration tests that covered diverse input function types such as numpy_input_fn and pandas_input_fn. The user's efforts ensured the robustness and compatibility of the Estimator API with different feature column configurations and input methods.
deep-learningmachine-learningtensorflowtensorflow-estimatorestimator
tensorflow/tensorflow
Jul 2016 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:75 reviews, 444 commits, 80 PRs in 6 years 5 months
Contributions summary:Rohan primarily contributed by adding and modifying test cases within the TensorFlow repository. Their work included creating new tests for op_kernel.cc, shape assertions in the LowerBound operation, and performance benchmarks for the ScaleAndTranslate operation. They also updated existing tests and made minor fixes to ensure code correctness and stability. This suggests a focus on quality assurance and thorough testing of TensorFlow functionalities.
pythondata-sciencedeep-learningmlmachine-learning
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