Ashwin J
Machine Learning Engineer at Pinterest
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAshwin J is a Machine Learning Engineer with eight years of experience building large-scale ML systems, currently shaping ads retrieval and query understanding at Pinterest Monetization in San Francisco. He combines industry and research pedigree from Enlitic and Carnegie Mellon, applying deep learning to medical imaging, adversarial face generation, and low-shot detection. Academically strong (CMU MS, high honors BTech) and hands‑on, he contributes open-source work in federated learning—implementing CNN/MLP architectures and end-to-end training for MNIST/CIFAR—demonstrating practical expertise in communication-efficient decentralized training. Comfortable moving models from research to production, he blends statistical rigor with product-focused engineering to solve real-world retrieval and inference challenges.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Technology (BTech), Computer Engineering, 3.93/4.0, Bachelor of Technology (BTech), Computer Engineering, 3.93/4.0 at Vellore Institute of TechnologyMaster's degree, Computer engineering, 3.85, Master's degree, Computer engineering, 3.85 at Carnegie Mellon University
English
Github Skills (9)
mask-rcnn10
faster-rcnn10
pytorch10
deep-learning10
python10
federated-learning9
mlp9
machine-learning9
mnist8
Programming languages (1)
Github contributions (5)
AshwinRJ/Federated-Learning-PyTorch
Nov 2018 - Mar 2021
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
Role in this project:
ML Engineer
ML EngineerContributions:63 commits, 5 PRs, 56 pushes in 2 years 4 months
Contributions summary:Ashwin contributed to the development of neural network architectures for federated learning, implementing both Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) models. The user added CNN architectures for MNIST and CIFAR datasets, demonstrating an understanding of different network structures and their application. Furthermore, the user implemented the main script that trains and tests these models, highlighting their role in building and evaluating machine learning models for the project.
pytorchdeep-networkspythoncommunication-efficientdeep-learning
AshwinRJ/Face-Generation-from-Voice
Mar 2019 - Nov 2019
Contributions:38 commits, 5 pushes in 8 months
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