Kai Huang
Senior Software Engineer at Apple
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolKai Huang is a Senior Software Engineer in San Francisco with 11 years of experience building scalable web architectures and distributed systems at companies including Apple, Twitter, and AWS. He has deep expertise in pub/sub and compute platform infrastructure from his years on Twitter’s Messaging and Compute Platform teams, and has contributed backend reliability enhancements to the well-known Apache Aurora scheduler, improving task state metrics and health-check driven restarts. Kai combines a strong academic foundation—MS in Computer Science—with earlier biomedical training, reflecting an analytical, measurement-driven approach to system design. Comfortable in both production engineering and open-source collaboration, he focuses on reliability, performance, and pragmatic solutions for large-scale services.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (BS), Biology, General, 3.86, Bachelor of Science (BS), Biology, General, 3.86 at Wuhan UniversityMaster of Science (MS), Computer Science, 3.897, Master of Science (MS), Computer Science, 3.897 at The University of Texas at DallasMaster of Science (MS), Biomedical Sciences, General, 3.90, Master of Science (MS), Biomedical Sciences, General, 3.90 at The University of Texas Southwestern Medical Center at Dallas
English, Chinese
Github Skills (11)
javas10
mesos10
java10
scheduler10
testing10
junit9
rest-api8
api-rest8
restful-api8
api-design8
python8
Programming languages (1)
Github contributions (5)
apache/aurora
Sep 2016 - Aug 2017
Apache Aurora - A Mesos framework for long-running services, cron jobs, and ad-hoc jobs
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 1 comment in 11 months
Contributions summary:Kai primarily contributed to the Apache Aurora scheduler's back-end, focusing on features related to task management and health checks. Their work included adding metrics for task state transitions (STARTING, RUNNING) and improving the scheduler's restart functionality. The user also made modifications to the executor's state transition logic, integrating health check mechanisms. Overall, the contributions focused on enhancing the scheduler's reliability and performance.
auroracron-jobsapachemesos-frameworklong
TexasRed/machine-learning
Feb 2015 - Apr 2016
Contributions:11 commits, 10 pushes in 1 year 2 months
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