Trieu Tran
DevOps Co-op at Schneider Electric
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTrieu Tran is a DevOps-focused software engineer and Boston University Computer Engineering graduate student with around a decade of hands-on experience building and deploying ML-powered systems. He combines production ML deployment chops—containerizing Jetson-based vision systems, deploying quantized LLMs with vLLM, and building PySpark/Fugue ETL pipelines—with practical DevOps skills at Schneider Electric. His open-source work includes meaningful contributions to darkflow, translating Darknet models to TensorFlow and improving core object-detection pipelines and data batching. Trieu thrives at the intersection of edge inference, backend APIs, and scalable data workflows, and brings a pragmatic, collaborative approach informed by field deployments on active farms. Notably, he has driven accuracy and reliability improvements in real-world constrained environments (91% tag ID accuracy) while also automating tests and validation to ensure robustness.
10 years of coding experience
1 year of employment as a software developer
International Baccalaureate (IB)IB Diploma, IB Diploma at Stuart Hall High SchoolMaster's degree Computer Engineering, Master's degree Computer Engineering at Boston University
Chinese, English, Spanish, French
Github Skills (7)
object-detection10
deep-learning10
tensorflow10
python10
machine-learning9
convolutional-neural-networks9
computer-vision9
Programming languages (5)
Github contributions (5)
thtrieu/darkflow
Aug 2016 - Feb 2020
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:304 commits, 42 PRs, 290 pushes in 3 years 6 months
Contributions summary:Trieu contributed to the project by implementing new features and improving existing ones within the Darkflow framework, which translates Darknet to TensorFlow. They introduced enhancements to the drawer module and made significant changes to the tfnet module, indicating work on the core object detection pipeline. Furthermore, the user's commits show the implementation of data processing for machine learning including the development of mini-batches.
tensorflowconstantreal-timeweightsobject-detection
thtrieu/essence
Dec 2016 - Feb 2020
Contributions:98 commits, 88 pushes, 1 branch in 3 years 2 months
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