Yuefeng Zhou
Co-Founder at Stealth AI Startup
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYuefeng Zhou is a seasoned AI infrastructure leader and entrepreneur with nine years of experience building distributed training systems and GenAI products, now co-founding a stealth AI startup in Mountain View. Previously a Staff Software Engineer at Google and Engineering Lead at Rhymes.AI, he led platform and applied GenAI efforts including distributed training APIs, model retrieval, LLM routing, finetuning for function calling, RAG pipelines, and agent interfaces. He has deep hands-on experience contributing to TensorFlow—improving estimator distributed execution and clarifying multi-worker and parameter-server docs—bridging low-level systems work with clear developer-facing documentation. Yuefeng combines research-grade systems thinking from CMU with product-led execution in startups, and is particularly skilled at turning complex distributed ML challenges into robust, usable infra.
9 years of coding experience
9 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Soochow University (CN)Master's Degree Information Networking, Master's Degree Information Networking at Carnegie Mellon UniversityExchange Student Computer Science, Exchange Student Computer Science at National Tsing Hua University
Github Skills (12)
keras10
machine-learning10
distributed-training10
deep-learning10
tensorflow10
python10
documentation10
model-optimization9
testing9
back-end-development9
resnet8
synthetic-data7
Programming languages (5)
Github contributions (5)
tensorflow/estimator
Oct 2018 - Jan 2021
TensorFlow Estimator
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 9 comments in 2 years 2 months
Contributions summary:Yuefeng primarily contributed to the `tensorflow/estimator` repository by modifying the `estimator.py` and `estimator_test.py` files. Their work involved integrating and optimizing distributed training strategies, specifically addressing issues related to the `distribute_coordinator_mode` and collective operations. The user also addressed lint errors and made changes to improve the efficiency of the system's execution of save and summary operations. Their changes suggest a focus on improving distributed training capabilities within the TensorFlow Estimator framework.
deep-learningmachine-learningtensorflowtensorflow-estimatorestimator
tensorflow/models
Dec 2016 - Jul 2020
Models and examples built with TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:15 commits, 28 PRs, 51 pushes in 3 years 7 months
Contributions summary:Yuefeng primarily contributed to the TensorFlow models, focusing on enhancements and bug fixes within the repository. Their work involved resolving issues with initializers and variable sharing, particularly in the context of batch normalization and ExponentialMovingAverage. They also added support for synthetic data generation within the Keras models and implemented features to tune thread parameters and distribution strategies for optimized performance.
deep-learningtensorflow
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