Yu-cheng Ling
Staff Software Engineer at Google
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolYu-cheng Ling is a Staff Software Engineer in the San Francisco Bay Area with 15 years of experience specializing in Machine Learning infrastructure and applied ML since 2017. At Google he led TensorFlow Lite projects—MLIR-based converter, control flow, delegates, on-device training and iOS support—and served as a Tech Lead Manager before moving to an undisclosed LLM role as an Applied ML Engineer. He combines deep systems-level skills in C++/C/Python and backend architectures with hands-on model training, tuning, evaluation and deployment to ship user-facing ML features. An active open-source contributor, he has fixed core language bugs and improved test coverage for Elixir and helped advance TensorFlow-to-CoreML tooling, reflecting a pragmatic focus on reliability and portability. Known for raising engineering rigor (e.g., dramatically improving test coverage in past teams), he blends technical leadership with IC-level execution across mobile, backend and ML stacks.
15 years of coding experience
11 years of employment as a software developer
MS, Computer Science, MS, Computer Science at National Tsing Hua University
Github Skills (12)
functional-programming10
tensorflow10
python10
elixir10
coreml10
testing10
machine-learning9
strings9
text-manipulation9
regular-expression9
pep9
list-comprehension8
Programming languages (8)
Github contributions (5)
tf-coreml/tf-coreml
Oct 2017 - Nov 2017
TensorFlow to CoreML Converter
Role in this project:
ML Engineer
ML EngineerContributions:12 commits in 1 day
Contributions summary:Yu-cheng primarily contributed to the project by modifying and formatting code related to the TensorFlow to CoreML conversion process. Their work involved loading and handling test images, removing trailing spaces, and formatting Python files according to PEP8 standards. Additionally, they made modifications to handle various aspects of layer conversion within the tfcoreml library.
coremltensorflowconverter
elixir-lang/elixir
Feb 2011 - Mar 2011
Elixir is a dynamic, functional language for building scalable and maintainable applications
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:21 commits in 16 days
Contributions summary:Yu-cheng primarily contributed to Elixir's core functionality by fixing bugs in the `OrderedDict` implementation and enhancing its test coverage. They also introduced new features like `String#to_atom` and `IO.gets` methods, demonstrating an understanding of the language's standard library. Furthermore, the user added new tests and refined existing ones for string manipulation, regular expressions, and list comprehensions, ensuring code quality and thorough testing. They also addressed interactive Elixir (IEX) improvements with multiline input and syntax error handling.
functional-languagescalablefunctional-programmingelixircompiler
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