Jiaxi Tang
Staff Research Engineer, Manager at Google DeepMind
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolJiaxi Tang is a Staff Research Engineer and manager at Google DeepMind, with 11 years of experience building applied ML systems that bridge large language models and recommender systems. He led personalization efforts for an AI assistant (Astra) and has driven LLM-for-recommendation work—adapting LLMs for user action modeling and building conversational recommendation agents with tool use, RAG, and memory. His background spans Google Brain and Google AI, with a PhD from Simon Fraser University focused on sequence-based user modeling and published work from internships at Google and the Chinese Academy of Sciences. An active contributor to open-source recommender tooling, he improved evaluation metrics and tests in the popular PyTorch spotlight repo, reflecting a pragmatic focus on model reliability and metrics. Based in Mountain View, he combines deep research rigor with product-facing engineering to deploy scalable personalization in consumer products.
11 years of coding experience
9 years of employment as a software developer
Bachelor's degree Software Engineering, Bachelor's degree Software Engineering at Wuhan UniversityDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Simon Fraser University
English, Chinese
Github Skills (8)
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
recommender-system10
testing10
numpy9
Programming languages (4)
Github contributions (5)
maciejkula/spotlight
Sep 2017 - Oct 2017
Deep recommender models using PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 1 PR, 7 comments in 1 month
Contributions summary:Jiaxi contributed to the `spotlight` repository, which focuses on deep recommender models. Their work involved implementing and testing precision and recall metrics, crucial for evaluating the performance of the recommendation models. They also corrected Python 3 print statements and made general code cleanup changes. The user further added a new test file, highlighting their focus on enhancing model evaluation and ensuring the accuracy of the recommender system's performance.
pytorchrecommenderrecommender-systemdeep-learninglearning-to-rank
graytowne/caser_pytorch
Jan 2018 - Dec 2018
A PyTorch implementation of Convolutional Sequence Embedding Recommendation Model (Caser)
Contributions:17 commits, 1 PR, 8 pushes in 1 year
pytorch
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