AnwaarΒ Khalid
Senior Engineer at Qualcomm
Bengaluru, Karnataka, India
Summary
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Top SchoolAnwaar Khalid is a senior engineer based in Bengaluru with six years of hands-on experience specializing in model compression and quantization for ML deployments. Currently part of Qualcomm's Centre of Excellence, he focuses on precision-aware optimizations that bridge research-grade models and efficient edge inference. He has led model-compression efforts as a founding member at Unify and briefly applied pricing-focused data science during an internship, reflecting a pragmatic blend of research and product thinking. Anwaar is an active open-source contributor to notable projects like mlpack and Ivy, where he implemented and tested core loss functions and improved numerical robustness across ML frontends. Collected experience across startups and industry R&D gives him a practical eye for stability, testing rigor, and preventing numeric pitfalls that often surface when scaling ML to production.
6 years of coding experience
Btech+Mtech, Computer Science, Btech+Mtech, Computer Science at Indian Institute of Information Technology Design & Manufacturing Kancheepuram
Stackoverflow
23reputation
1kreached
0answers
5questions
Github Skills (21)
loss-functions10
c-language10
machine-learning-models10
testing10
machine-learning10
web-framework10
cprogramming-language10
numpy9
tensorflow9
jax8
logistic-regression6
mlpack6
deep-learning6
archlinux6
cmake6
Programming languages (8)
Github contributions (5)
mlpack/mlpack
Jan 2022 - Feb 2022
mlpack: a fast, header-only C++ machine learning library
Role in this project:
ML Engineer
ML EngineerContributions:11 reviews, 39 commits, 8 PRs in 1 month
Contributions summary:Anwaar primarily contributed to the implementation and testing of various loss functions within the mlpack machine learning library. They added and updated loss function classes, including L1Loss, MeanBiasError, HuberLoss, KLDivergence, and more. Their work involved defining forward and backward passes, and they also ensured the correct behavior through testing.
cppmachine-learningmlpackmachine-learning-libraryc-plus-plus
unifyai/ivy
Nov 2022 - Jan 2023
Convert Machine Learning Code Between Frameworks
Role in this project:ML Engineer
ML EngineerContributions:355 reviews, 17 commits, 126 PRs in 2 months
Contributions summary:Anwaar made several contributions focused on testing and improving the accuracy and robustness of the Ivy machine learning framework, primarily concerning the NumPy frontend. This included adding tolerance parameters to tests for functions like `np.average` and `np.mod`, and fixing tests for functions such as `jax.lcm` and `jax.nn.selu`. The user also modified and improved tests related to Tensorflow functions, specifically those related to power operations, argmax, truediv and others, and optimized some tests in order to prevent overflow errors.
machine-learningpythontensorflowpytorchnumpy
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