Karen Wu
AI Software Engineering Manager at Intel Corporation
Saratoga, California, United States
Summary
👤
Senior🎓
Top SchoolKaren Wu is an AI software engineering manager and seasoned test automation expert with over 15 years of experience designing test frameworks, leading release cycles, and validating large-scale enterprise and RESTful service architectures. At Intel she blends hands-on MLOps and build/release work—contributing performance and stability improvements to high-profile projects like Intel AI Reference Models and TensorFlow—with team leadership and cross-border mentoring. Her technical toolkit spans Java, Perl, Unix scripting, TestNG/ANT, and CI/dependency management, enabling her to drive both quality and process improvements across Agile teams. Known for pragmatic problem solving and attention to detail, she pairs deep QA methodology with practical engineering chops to move complex ML systems from validation into reliable production.
8 years of coding experience
12 years of employment as a software developer
San José State University
Github Skills (16)
dependency-management10
build-system10
performance-monitor10
performance-analysis10
inference10
tensorflow10
deep-learning10
python10
cpu8
ai8
c117
c177
docker6
system-security6
dockers6
Programming languages (4)
Github contributions (5)
intel/ai-reference-models
Aug 2019 - Apr 2020
Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs
Role in this project:
MLOps Engineer
MLOps EngineerContributions:5 commits in 8 months
Contributions summary:Karen's commits primarily involve integrating and optimizing existing machine-learning models within the Intel AI Reference Models repository. They are focused on performance improvements, which include reverting multi-instance implementations. The user's work is geared towards maintaining the stability and performance of inference-related components and optimizing the models for improved efficiency. Additionally, the user is involved in applying and testing model optimizations.
optimizationsprocessorstensorflowzoomodel-zoo
tensorflow/tensorflow
Jun 2020 - Feb 2021
An Open Source Machine Learning Framework for Everyone
Role in this project:DevOps Engineer & Build & Release Engineer
DevOps Engineer & Build & Release EngineerContributions:1 review, 7 commits, 9 PRs in 8 months
Contributions summary:Karen primarily focused on updating and maintaining third-party dependencies, specifically `curl` and `sqlite`. Their contributions involved upgrading these libraries to newer versions, including CVE fixes, and ensuring proper integration within the TensorFlow build system. They also addressed missing files related to the `schannel` security protocol. This work indicates a focus on build system maintenance and dependency management within the project.
pythondata-sciencedeep-learningmlmachine-learning
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