Sachin Goyal is a PhD-trained machine learning researcher and Member of Technical Staff based in San Francisco with nine years of experience building and optimizing ML systems from edge devices to large-scale pretraining. His work spans industry research roles at Anthropic, Meta, Google and Microsoft, where he added GPU support and performance features to EdgeML models like ProtoNN and SRNN for resource-constrained deployments. At CMU he focused on machine learning optimization algorithms and robust LLM training, bridging theoretical research with practical engineering. He has published medical imaging work that improved MRI resolution and brings a knack for squeezing performance out of tight compute budgets—an asset reflected in his contributions to a well-known Microsoft Research EdgeML repo.
9 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology Bombay
passed 12 with 93%., passed 12 with 93%. at Maharana Mewar Public School
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Carnegie Mellon University
This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
Role in this project:
ML Engineer
Contributions:2 reviews, 11 commits, 24 PRs in 1 month
Contributions summary:Sachin primarily focused on enhancing the `ProtoNN` and `SRNN` models within the `pytorch_edgeml` library, specifically by adding GPU support. Their contributions included modifying trainer classes and example scripts to utilize CUDA, improving performance. They also added support for dropout and FastCell features to the SRNN model.
Contributions:51 commits, 110 pushes, 3 branches in 2 years 4 months
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Sachin Goyal - Member Of Technical Staff at Anthropic