Jiaming Zeng
Senior Research Engineer at Google DeepMind
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJiaming Zeng is a Senior Research Engineer at DeepMind with nine years of experience translating cutting-edge ML research into scalable, production-grade systems, currently focused on Personalization and applied Generative AI for the Gemini app. He has led high-impact NL2SQL and Conversational Analytics efforts at Google, launching features like self-consistency that measurably increased BigQuery query acceptance and executability, and architected Looker Query generation to GA. At AKASA he built and deployed the company’s first clinical-text-specialized LLMs and end-to-end pipelines for T5/MPT, applying generative models to healthcare with product and patent-level impact. His academic work spans a Stanford PhD and postdoctoral research on causal inference, fairness, and interpretable models in clinical settings, which informs his emphasis on ethically robust systems. An active contributor to TensorFlow Probability through Bayesian neural network implementations, he blends rigorous probabilistic modeling with pragmatic engineering for reliable ML. Based in San Francisco, he pairs deep research roots with product delivery experience, often bridging gaps between clinical NLP, causal methods, and large-scale GenAI deployments.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Science Mathematics with Computer Science, Bachelor of Science Mathematics with Computer Science at Massachusetts Institute of TechnologyDoctor of Philosophy (Ph.D.) Management Science and Engineering, Doctor of Philosophy (Ph.D.) Management Science and Engineering at Stanford University
English, Chinese, German
Github Skills (10)
neural-network10
bayesian-network10
machine-learning10
convolutional-neural-networks10
tensorflow10
tensorflow-probability10
python9
deep-learning9
bayesian-methods8
data-science8
Programming languages (3)
Github contributions (5)
tensorflow/probability
Jul 2018 - Sep 2018
Probabilistic reasoning and statistical analysis in TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:23 commits, 2 PRs, 23 comments in 1 month
Contributions summary:Jiaming primarily contributed to the implementation and maintenance of a Bayesian neural network (BNN) model for the MNIST dataset within the TensorFlow Probability framework. Their commits focused on integrating the Flipout Monte Carlo estimator for the convolution and fully-connected layers, along with setting up the architecture, loss calculations, and evaluation metrics. The user also addressed code style issues, added comments, and updated the codebase to meet TensorFlow Probability guidelines.
statisticspythonprobabilistic-reasoningdata-sciencedeep-learning
jmzeng/CS229-TetrisIsAwesome
Nov 2016 - Oct 2017
Contributions:58 pushes, 1 branch in 10 months
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