Jian Jiao
Partner Software Engineering Manager, Microsoft Advertising Engineering
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJian Jiao is a Partner Software Engineering Manager at Microsoft Advertising with a decade of experience building and scaling ML- and NLP-driven relevance and recommendation systems. He progressed from SDE and deep learning roles to leading applied science and engineering teams that deliver online/offline solutions across NLU, NLG, GNNs, extreme classification, IR, and causal inference for ads selection and creative generation. Jian combines hands-on research chops—a PhD in Computer Science from Virginia Tech—with practical production engineering, and has contributed ranking-loss implementations (IRMetricV3, LambdaRank) to the notable Microsoft Cognitive Toolkit (CNTK). Based in Bellevue, he focuses on improving match quality, ad relevance and multimedia ad generation at scale while mentoring cross-functional teams to translate novel algorithms into robust production features. Colleagues know him for bridging rigorous ML research with pragmatic engineering to impact millions of ad impressions.
10 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Virginia TechM.S. Computer Science, M.S. Computer Science at Beijing Institute of Technology
English, Chinese
Github Skills (11)
neural-network10
machine-learning10
c-language10
cntk10
deep-learning10
cprogramming-language10
ranking9
python5
cplus5
deep-neural-networks5
cpp5
Programming languages (2)
Github contributions (2)
microsoft/CNTK
May 2016 - Dec 2017
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
ML Engineer
ML EngineerContributions:60 commits, 49 pushes, 14 branches in 1 year 7 months
Contributions summary:Jian contributed to the implementation of IRMetricV3 and LambdaRankNode, focusing on ranking metrics within the CNTK deep learning toolkit. The commits involve changes in the `TrainingNodes.h` and `EvaluationNodes.h` files, which suggests the integration of new loss functions and evaluation metrics. The modifications include code for calculating gradients, updating function MB sizes, and aggregating results at a query level, directly impacting the model training and evaluation processes.
pytorchpythondeep-learningc-plus-plusmachine-learning
huggingface/transformers
Feb 2020 - Feb 2020
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Contributions:1 issue in 1 day
pythonbertspeech-recognitionstate-of-the-artflax
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