Sachin Goyal

Member Of Technical Staff at Anthropic

San Francisco, California, United States
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

🤩
Rockstar
🎓
Top School
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.
code9 years of coding experience
job4 years of employment as a software developer
bookIndian Institute of Technology Bombay
bookpassed 12 with 93%., passed 12 with 93%. at Maharana Mewar Public School
bookDoctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Carnegie Mellon University
github-logo-circle

Github Skills (8)

pytorch10
machine-learning10
deeplearning-ai10
rnn-model10
deep-learning10
gpu10
n10
cpp3

Programming languages (3)

C++Jupyter NotebookPython

Github contributions (5)

github-logo-circle
microsoft/EdgeML

Jul 2019 - Aug 2019

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
Role in this project:
userML 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.
classifieredge-machine-learningsensortensorflowmicrosoft
Contributions:51 commits, 110 pushes, 3 branches in 2 years 4 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Sachin Goyal - Member Of Technical Staff at Anthropic