Anish Mahishi
Machine Learning Engineer at Bloomberg
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAnish Mahishi is a Machine Learning Engineer with nine years of experience building production-ready ML systems and research-driven deep learning solutions, currently working on search at Bloomberg. Trained at IIT Delhi (Maths & Computing) and NYU (MS CS, 3.91 GPA), he blends strong theoretical foundations with practical engineering in Python, C++ and Java. His open-source contributions to PyTorch include designing and benchmarking data sparsifiers—enabling an 80% embedding compression on DLRM with minimal accuracy loss—showcasing expertise in model compression and scalable ML infrastructure. At Fractal Analytics he delivered customer cadence and recommendation systems that moved key business metrics in A/B tests and deployed real-time models on Kubernetes. He pairs hands-on kernel-level PyTorch work with end-to-end product deployments, and has a track record of translating research prototypes into measurable business impact.
9 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)Masters, Computer Science, 3.91, Masters, Computer Science, 3.91 at New York University
English, Hindi, Kannada
Stackoverflow
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Github Skills (8)
pytorch10
machine-learning10
tensor10
deep-learning10
embedding10
python10
data-science9
testing9
Programming languages (2)
Github contributions (5)
pytorch/pytorch
Jun 2022 - Aug 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:72 reviews, 65 commits, 32 PRs in 2 months
Contributions summary:Anish primarily contributed to the implementation and testing of data sparsifiers within the PyTorch framework. Their work included the development of a "Nearly Diagonal Sparsifier" and the integration of data sparsification techniques for embeddings and embedding bags. The user also created and refined base classes and utility functions for more general data sparsification processes. Furthermore, they developed benchmarking tools to evaluate disk space savings and model quality metrics by introducing sparsity in the DLRM model.
pythongpu-accelerationdeep-learninggpunumpy
macandro96/Artificial-Neural-Network-
Dec 2018 - Dec 2019
Contributions:1 push, 1 branch in 1 year
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