Hyeontaek Lim
Senior Staff Research Scientist at Google DeepMind
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolHyeontaek Lim is a Senior Staff Research Scientist with 15 years of experience building high-performance, scalable ML infrastructure across frameworks, runtimes, and compilers. At Google DeepMind he helped develop Pathways and IFRT and contributed to the software stack behind PaLM, PaLM 2, and Gemini, bridging research-grade models with production runtimes. His hands-on open-source work includes implementing the JAX transfer guard and IFRT/PjRt integration in TensorFlow and XLA, improving visibility and control over host-device data movement—an often-overlooked source of performance pain. Trained with a PhD from Carnegie Mellon and seasoned through roles from postdoc to senior research scientist at Google, he thrives at cross-layer systems design that turns compiler and runtime advances into tangible ML throughput gains.
15 years of coding experience
8 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Carnegie Mellon University
Github Skills (19)
c-language10
pytest10
python10
apidoc10
testing10
api10
tensorflow10
xla10
compiler10
jax10
cprogramming-language10
debug9
debugging9
numpy9
ml9
Programming languages (4)
Github contributions (5)
openxla/xla
Feb 2022 - Jan 2023
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:
Back-end Developer
Back-end DeveloperContributions:23 commits in 11 months
Contributions summary:Hyeontaek implemented the JAX transfer guard API, adding features for logging and disallowing unintended data transfers within the XLA framework. This involved defining transfer guard levels, actions, and direction-specific controls. The changes included modifications to the core C++ library, along with updates to the header files. The user's work directly impacts the control and visibility of data movement within the XLA compilation process, impacting the performance and debugging capabilities of the system.
compilercommunity-drivenmachine-learningmodular
jax-ml/jax
Feb 2022 - Dec 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 13 commits, 3 PRs in 9 months
Contributions summary:Hyeontaek implemented the JAX transfer guard API, adding functionality to control data transfers between the host and devices. This involved adding a command-line flag and context manager to allow logging, disallowing, or explicitly managing transfers. The changes included defining different transfer guard levels (allow, log, disallow, log_explicit, disallow_explicit) and applying them to host-to-device, device-to-device, and device-to-host transfers. Further contributions include documentation and tests to verify the transfer guard's behavior.
pytorchpythonjitautomatic-differentiationgpu
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