Gaurav Pathak
Machine Learning Engineer at Adobe
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolGaurav Pathak is a Machine Learning Engineer in the San Francisco Bay Area with 11 years of experience focused on generative models and computer vision, currently working on diffusion models and distributed training at Adobe. He holds an MS in Robotics from Carnegie Mellon and brings a research-first mindset from years as a CMU research assistant and earlier industry roles building NLP and VoIP products. Gaurav contributes to core ML tooling—his work in the JAX repository added statistical distributions and robust API/shape checks—demonstrating comfort with numerical library internals and performance-sensitive code. He combines academic rigor with practical production experience, thriving on scalable, applied ML challenges that bridge research and engineering.
11 years of coding experience
6 years of employment as a software developer
Mount Carmel Convent High SchoolMaster of Science - MS, Robotics, Master of Science - MS, Robotics at Carnegie Mellon UniversityBachelor of Engineering (BE), Electrical, Electronics and Communications Engineering, Bachelor of Engineering (BE), Electrical, Electronics and Communications Engineering at Smt.Kashibai Navale college of Engineering
English, gujrathi, Marathi, Hindi
Github Skills (8)
machine-learning10
jax10
python10
scientific-computing10
numpy10
api-design9
linear-algebra9
testing8
Programming languages (8)
Github contributions (5)
jax-ml/jax
Jan 2021 - Jan 2021
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 6 commits, 5 PRs in 3 days
Contributions summary:Gaurav contributed to the JAX library, focusing on extending its functionality for scientific computing and machine learning. They added support for statistical distributions (geometric distribution), implemented mathematical functions like `diagflat` and `polymul`, and added a function to remove leading zeros (`trim_zeros`). Furthermore, the user was involved in improving the JAX's API, specifically fixing issues related to shape and type checking within the JVP function, demonstrating contributions to both core mathematical functionality and API robustness.
pytorchpythonjitautomatic-differentiationgpu
gsp-27/pytorch_Squeezenet
Jan 2017 - May 2020
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come
Contributions:21 commits, 1 PR, 17 pushes in 3 years 4 months
pytorchloss-functionssqueezenetefficientnetpretrained-models
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