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Eunjae Kim

PROFILE

Eunjae Kim

Worked on core deep learning infrastructure, delivering targeted improvements to both the google/flax and tensorflow/tensorflow repositories. Addressed numerical stability in flax by implementing a fix to preserve parameter data types in normalization layers, enhancing precision and reproducibility during mixed-precision training. In tensorflow/tensorflow, developed features to improve StableHLO integration with internal compilers, introducing attribute-driven control over XlaCallModule replacements to support quantization workflows. Leveraged C++, MLIR, and Python to implement granular transformation controls and comprehensive test coverage, resulting in more reliable and maintainable machine learning pipelines. Demonstrated depth in compiler design, library development, and numerical computation throughout the work.

Overall Statistics

Feature vs Bugs

67%Features

Repository Contributions

4Total
Bugs
1
Commits
4
Features
2
Lines of code
163
Activity Months3

Work History

June 2025

1 Commits • 1 Features

Jun 1, 2025

June 2025 monthly summary for tensorflow/tensorflow focusing on feature delivery and stability improvements. Implemented granular control to disable StableHLO->XLA replacement via the _no_xla_call_module attribute, updated the function replacement pass to honor the attribute, and added tests to validate behavior. Fixed forward for the broken tests related to this feature and stabilized the test suite for this transformation workflow.

May 2025

2 Commits • 1 Features

May 1, 2025

May 2025 monthly summary for tensorflow/tensorflow focusing on feature delivery and impact. The period delivered key stability and integration improvements in StableHLO with selective XlaCallModule replacement, enhancing compatibility with quantization workflows and internal compiler usage. No explicit bug fixes were reported in the provided data; the emphasis was on feature delivery and internal tooling visibility.

October 2024

1 Commits

Oct 1, 2024

Implemented a precise, minimal-impact fix to preserve parameter dtype in normalization layers across flax, improving numerical stability and consistency in mixed-precision training.

Activity

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Quality Metrics

Correctness97.6%
Maintainability87.6%
Architecture90.0%
Performance85.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++MLIRPython

Technical Skills

C++ developmentC++ programmingDeep LearningLibrary DevelopmentMachine LearningNumerical ComputationTensorFlowcompiler designinternal toolingmachine learningquantization

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

tensorflow/tensorflow

May 2025 Jun 2025
2 Months active

Languages Used

C++MLIRPython

Technical Skills

C++ programmingTensorFlowcompiler designinternal toolingmachine learningquantization

google/flax

Oct 2024 Oct 2024
1 Month active

Languages Used

Python

Technical Skills

Deep LearningLibrary DevelopmentMachine LearningNumerical Computation