Jian Guo
Software Engineer at Google
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolJian Guo is a software engineer based in the San Francisco Bay Area with 10 years of experience building production-grade systems in streaming, pipeline management, and machine learning. Currently at Google, he brings deep practical expertise in object detection and model engineering, having contributed key improvements to high-profile open-source projects like GluonCV and Apache MXNet (including ROI pooling, Faster R-CNN enhancements, and training stability fixes). His background spans applied research and product-focused roles, including internships at Amazon and TuSimple, and an MS from the University of Michigan supporting his applied ML foundation. Jian combines systems thinking with hands-on optimization—rewriting samplers, refactoring detection pipelines, and adding pragmatic parameters to improve real-world model behavior. He is comfortable navigating both research codebases and large-scale production constraints, helping bridge experimental models into reliable infrastructure. A detail-oriented engineer, he often focuses on training stability and reproducibility—work that has directly improved widely used detection toolkits.
10 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Master of Science - MS at University of MichiganBachelor of Engineering - BE, Bachelor of Engineering - BE at Beihang University
Github Skills (13)
mask-rcnn10
object-detection10
computer-vision10
mxnet10
faster-rcnn10
deeplearning-ai10
machine-learning10
deep-learning10
python10
cprogramming-language9
c-language9
cuda9
tensorflow8
Programming languages (4)
Github contributions (5)
apache/mxnet
Apr 2016 - Jul 2018
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 13 PRs, 133 comments in 2 years 3 months
Contributions summary:Jian contributed to the implementation of a ROI pooling operator, a crucial component for object detection models. They also updated an existing example to utilize Faster R-CNN, demonstrating their understanding of modern object detection architectures. Furthermore, the user added a `valid_thresh` parameter to the `contrib.box_nms` operation, indicating an effort to improve existing functionality.
pythonschedulerdataflowmutationdata-science
dmlc/gluon-cv
Jun 2018 - Aug 2018
Gluon CV Toolkit
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 6 PRs, 15 comments in 2 months
Contributions summary:Jian made several significant contributions to the Gluon CV toolkit. They focused on improving Faster R-CNN and Mask R-CNN models by adding and modifying configurations for the COCO dataset. The user addressed issues related to batch normalization, implemented various fixes for training stability and model performance, and added pretrained models. Their work also involved refactoring code, rewriting samplers, and optimizing different components within the object detection framework.
pytorchmxnetperson-reidsemantic-segmentationdeep-learning
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