Dhruv Nair
Machine Learning Engineer at Hugging Face
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolDhruv Nair is a Machine Learning Engineer with 11 years of experience building production-ready ML systems, currently contributing to diffusion model development at Hugging Face. He joined Hugging Face after five years at Comet ML and earlier research roles at IBM, blending research rigor with product-focused engineering. Dhruv’s open-source contributions to the high-profile huggingface/diffusers repo include improving Stable Diffusion pipelines and stabilizing tests—work that tightens core model behavior and reliability. Trained as a mechanical engineer at Georgia Tech and Columbia, he brings a systems-thinking perspective to ML infrastructure and model internals. Based in Bengaluru, he specializes in back-end ML engineering with a knack for making complex generative models more robust and user-friendly.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.Sc.), Mechanical Engineering, Bachelor of Science (B.Sc.), Mechanical Engineering at Georgia Institute of TechnologyMaster's degree, Mechanical Engineering, Master's degree, Mechanical Engineering at Columbia University in the City of New York
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (14)
pytorch10
machine-learning10
machine-learning-models10
diffusers9
modeling9
testing9
trainings9
diffusion-probabilistic-models9
image-generation9
diffusion-models9
diffusion-probabilistic9
deep-learning8
stable-diffusion8
deeplearning-ai8
Programming languages (4)
Github contributions (5)
huggingface/diffusers
Feb 2023 - Apr 2025
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:993 reviews, 539 PRs, 1006 pushes in 2 years 2 months
Contributions summary:Dhruv's commits primarily focused on modifying the `StableDiffusionInstructPix2PixPipeline` to handle prompt embeddings correctly, allowing for prompts to be passed in without prompts. They also contributed to fixing flaky tests within the Stable Diffusion inpainting pipeline, including setting default attention processors and adjusting precision checks. These changes suggest work related to the model's core functionality and test stability, which aligns with back-end development and ML engineering roles.
pytorchartdeep-learningimage2imagestate-of-the-art
comet-ml/issue-tracking
Jul 2018 - Oct 2021
Questions, Help, and Issues for Comet ML
Contributions:25 commits, 19 PRs, 14 pushes in 3 years 3 months
pythoncometdata-sciencedeep-learningmachine-learning
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.