Qinqing Zheng
Member Of Technical Staff at Inception
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolQinqing Zheng is a machine learning researcher-engineer with 10 years of experience building scalable ML systems and advancing generative AI, currently a Member of Technical Staff in Mountain View. Her work spans reinforcement learning, LLM reasoning, diffusion models and LLaMA post-training, blending deep research (FAIR/Meta, Wharton postdoc) with production-focused engineering such as distributed training for recommendations. She has a strong systems bent—contributing GPU-accelerated operators and momentum SGD to the widely used Caffe2 framework—bringing both algorithmic insight and low-level performance optimizations. Qinqing holds a PhD in Computer Science from the University of Chicago, with formative studies at Max Planck Institute and Zhejiang University, and routinely navigates privacy, federated learning and statistical inference problems. Colleagues describe her as equally comfortable prototyping novel models and hardening them for real-world deployment, often surfacing subtle implementation details that materially improve robustness and efficiency.
10 years of coding experience
8 years of employment as a software developer
Master of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Max Planck Institute for InformaticsDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of ChicagoBachelor’s Degree, Digital Media, Bachelor’s Degree, Digital Media at Zhejiang University
Github Skills (9)
cuda10
gpu-programming10
machine-learning10
caffe10
deep-learning10
ai9
c-language8
python8
cprogramming-language8
Programming languages (4)
Github contributions (5)
facebookarchive/caffe2
Oct 2017 - Mar 2018
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:
ML Engineer
ML EngineerContributions:47 commits, 19 PRs, 9 pushes in 5 months
Contributions summary:Qinqing primarily focused on implementing and improving the `WeightedSample` operator within the Caffe2 deep learning framework. Their work involved adding CUDA support to the operator, enabling GPU acceleration for weighted sampling operations, and optimizing the CPU version. Furthermore, the user extended the operator to support non-normalized weights, improving its flexibility. The user also contributed to momentum SGD implementation.
pytorchscalablecaffe2deep-learningml
enosair/federated-fdp
Feb 2021 - Jul 2021
Contributions:3 commits, 2 pushes, 1 comment in 4 months
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