Kurtis Liu is a Full Stack Software Engineer with 13 years of experience specializing in image and video encoding, machine learning, and computer vision, currently based in Seattle. He pairs graduate-level expertise in ML/CV with low-level, pixel-level optimizations implemented in C++, TensorFlow, CUDA, and OpenCL, and has practical embedded-systems experience tackling race conditions and tight CPU/memory constraints. Kurtis has shipped production-grade systems at Amazon and LinkedIn and contributes to open-source infrastructure—most notably improving metrics and portable bundle management for the Apache Beam Samza runner. He also builds developer tooling like ReRe, a Java object record-and-replay project that exports Mockito mocks, demonstrating an appetite for practical developer productivity solutions. Comfortable moving between research prototypes and hardened back-end code, he brings both algorithmic rigor and systems-level pragmatism. An understated strength is his history of squeezing performance from constrained environments, a skill that informs his work across cloud, embedded, and GPU-accelerated domains.
13 years of coding experience
8 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at University of Wisconsin-Madison
Bachelor's degree, Mathematics and Computer Science, Bachelor's degree, Mathematics and Computer Science at National Taiwan University
Apache Beam is a unified programming model for Batch and Streaming data processing.
Role in this project:
Back-end Developer
Contributions:42 reviews, 1 commit, 11 PRs in 1 day
Contributions summary:Kurtis primarily contributed to the Apache Beam project, focusing on the Samza runner implementation. Their work involved adding support for metrics related to DoFn processing, including counters, gauges, and distributions. They refactored code related to bundle management and implemented a portable bundle manager to enhance the project's capabilities. These changes improve the metrics collection and the overall efficiency of the Samza runner.
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