Hieu Pham
Member Of Technical Staff at OpenAI
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHieu Pham is a research-driven software engineer with 11 years of experience building and deploying ML systems across industry-leading labs and startups, currently a Member of Technical Staff at OpenAI after roles at xAI, Augment, and Google AI. He holds a PhD in Machine Learning from Carnegie Mellon and a CS bachelor’s from Stanford, blending deep academic grounding with hands-on engineering. His work spans neural architecture search and model training infrastructure—evidenced by contributions to the ENAS TensorFlow codebase where he improved core model definitions, training scripts, and controller/data configs for better performance. Comfortable moving models from research prototyping to production, he focuses on practical optimizations in convolutional and controller logic that yield measurable gains. Based in Mountain View, he combines curiosity-driven research with product-oriented delivery and a track record of improving ML pipelines behind the scenes.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Carnegie Mellon UniversityBachelor's degree Computer Science, Bachelor's degree Computer Science at Stanford University
Github Skills (11)
neural-network10
machine-learning10
convolutional-neural-networks10
trainings10
tensorflow10
python10
nas10
modeling10
neural-architecture-search10
deep-learning9
data-processing8
Programming languages (3)
Github contributions (5)
melodyguan/enas
Apr 2018 - May 2019
TensorFlow Code for paper "Efficient Neural Architecture Search via Parameter Sharing"
Role in this project:
ML Engineer
ML EngineerContributions:23 commits, 11 pushes, 30 comments in 1 year 1 month
Contributions summary:Hieu primarily contributed to the core model definition and training scripts. Their changes include modifications to the `GeneralChild` model, which involves adjustments to convolutional layers, pooling operations, and branching logic. Furthermore, the user addressed syntax errors and updated the configuration files, specifically related to the model architecture, training parameters, and evaluation setup. They also updated the controller and data input parameters for the PTB model, optimizing it for better performance.
deep-learningneural-architecture-searchsharingparameterneural-architecture
hyhieu/hyhieu.github.io
Jan 2017 - Nov 2024
Contributions:273 pushes, 1 branch in 7 years 11 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.