Xiao Yu
San Francisco, California, United States
Summary
🤩
RockstarXiao Yu is a software engineer with 14 years of industry experience, currently at Google in Sunnyvale, specializing in high-performance ML runtimes and TensorFlow infrastructure. He has driven backend and tracing enhancements across flagship TensorFlow projects (runtime, estimator, core, and TensorBoard), including initial @tf.function support in TFRT and work enabling multi-partition GPU and TPU compatibility. His career spans foundational roles at Amazon across consumer services and app platforms, giving him strong production engineering and systems debugging chops. Notably, he blends deep runtime-level insight with practical ML tooling improvements, improving observability and backward compatibility in widely used open-source ML components.
14 years of coding experience
Github Skills (20)
debugging10
c-language10
python10
testing10
machine-learning10
tpu10
tensorflow10
profiling10
cprogramming-language10
apidoc9
deeplearning-ai9
api9
deep-learning9
keras9
user-interface8
Programming languages (4)
Github contributions (5)
tensorflow/runtime
May 2020 - Oct 2021
A performant and modular runtime for TensorFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:52 commits in 1 year 5 months
Contributions summary:Xiao primarily focused on enhancing the TensorFlow runtime by addressing tracing API issues and incorporating trace activities within the CoreRuntime's MakeOp API. They also contributed to backward compatibility by adding support for the `Unsupported` data type. Furthermore, the user implemented the initial support for `@tf.function` within TFRT, including making necessary modifications to the core runtime.
tensorflow
tensorflow/estimator
May 2019 - Sep 2021
TensorFlow Estimator
Role in this project:ML Engineer
ML EngineerContributions:7 commits in 2 years 4 months
Contributions summary:Xiao contributed to the TensorFlow Estimator library, making changes related to TPU integration and evaluation. Their work involved modifying test cases, including those for gradients and input pipelines, to account for TPU compatibility, particularly related to TFRT and embedding support. They also refactored code to explicitly initialize the TPU system and added metrics to track estimator API usage.
tensorflow-estimator
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