Amit Agarwal
Software Engineer at Meta
Menlo Park, California, United States
Summary
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Rockstar🎓
Top School
Top expert inMicrosoft Technology Ecosystem Development
Amit Agarwal is an experienced software engineer with 11 years building large-scale web products, currently contributing to Facebook’s video publishing platform at Meta. He combines front-end expertise in JavaScript, React/Redux and accessibility with back-end Node.js skills, having led teams and shipped end-to-end features from inception to production while running A/B experiments. His background spans startups to large companies (Google, Yahoo, Walmart Labs), where he focused on web performance, scalability and cross-functional collaboration—he even led Walmart’s Home vertical and built walmart.com/home. Beyond web apps, he has contributed performance and distributed-training improvements to Microsoft’s CNTK, demonstrating competence in distributed systems and sparse matrix handling. Based in Menlo Park, Amit pairs hands-on engineering with mentoring and hiring, and brings a pragmatic product-driven mindset to complex engineering challenges.
11 years of coding experience
11 years of employment as a software developer
B.Tech., Information and Communication Technology, B.Tech., Information and Communication Technology at Dhirubhai Ambani Institute of Information and Communication Technology
Stackoverflow
4,400reputation
367kreached
3answers
30questions
Github Skills (23)
c-language10
sparse-matrix10
mpi10
machine-learning10
distributed-systems10
cntk10
parallel-computing10
cprogramming-language10
deep-learning9
algorithms7
data-structure7
data-structures7
html6
css-animations6
firefox6
Programming languages (2)
Github contributions (5)
microsoft/CNTK
Mar 2015 - Feb 2019
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1126 commits, 2 PRs, 797 pushes in 3 years 11 months
Contributions summary:Amit's commits focus on modifying the source code of the CNTK library, with a focus on performance enhancements and new features. Specifically, the user has been working on the integration of MPI and other tools for parallel distributed training of machine learning models, showing expertise in distributed systems. The user's code changes also touch upon improving support for sparse matrices, with modifications involving matrix operations and memory management. Furthermore, the user has contributed to improvements in the training code, adding mechanisms for sequence-based learning.
pytorchpythondeep-learningc-plus-plusmachine-learning
CNTK-components/CNTK1bitSGD
Apr 2016 - Dec 2016
Contributions:7 commits in 8 months
cntkcomputationalmicrosoft
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