Rohit Srivastava
Deep Learning Engineer at Amazon Web Services
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRohit Srivastava is a Deep Learning Engineer with nine years of experience focused on accelerating tensor- and matrix-based computations across GPUs and multi-CPU environments. Based in Palo Alto, he works at AWS on MXNet, Elastic Inference and Neo/TVM, bringing practical expertise in making ML operators reliable and performant for production. His contributions to the flagship Apache MXNet project include stabilizing flaky tests, implementing large-tensor operators, and improving operator performance—work that directly impacts distributed and mobile deep learning deployments. Rohit pairs research-caliber rigor from his MS at The Ohio State University with hands-on systems engineering across Amazon and Expedia, and he has a track record of fixing hard-to-reproduce test and operator bugs that improve platform robustness.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Engineering (B.E.), Computer Engineering, Bachelor of Engineering (B.E.), Computer Engineering at Netaji Subhas Institute of TechnologyMaster of Science (M.S.), Computer Science and Engineering, 3.72, Master of Science (M.S.), Computer Science and Engineering, 3.72 at The Ohio State University
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
tensorrt10
unit-testing10
tensor10
tensorflow10
operation10
python10
test-automation10
machine-learning9
deep-learning9
Programming languages (5)
Github contributions (5)
apache/mxnet
Jul 2018 - Mar 2021
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:107 reviews, 96 commits, 171 PRs in 2 years 8 months
Contributions summary:Rohit primarily contributed to enhancing the Apache MXNet project by addressing and resolving issues related to test flakiness. They disabled failing unit tests and adjusted parameters within existing tests to increase their reliability. Furthermore, the user implemented new operators and added tests to verify support for large tensors, and also made modifications to improve the operator’s performance and fixed bugs.
pythonschedulerdataflowmutationdata-science
access2rohit/deep-learning-containers
Mar 2023 - Jan 2024
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Contributions:102 pushes, 6 branches in 9 months
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