Zhengyu C
Research Scientist at TikTok
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolZhengyu C is a research scientist based in San Jose with a decade of experience building production-grade ML systems, currently focused on large language and multimodal foundation models at ByteDance/TikTok. Trained at Tsinghua and Carnegie Mellon with cross-continental research stints, he combines deep academic rigor with hands-on MLOps and systems engineering. His background spans perception and autonomy at TuSimple, scalable data tooling at A9, and open-source contributions to Kubernetes-centric projects like Koordinator and Kubeflow that improve runtime scheduling and distributed training reliability. Equally comfortable in C++ for embedded GPU deployments and Python/PyTorch for model work, he has a proven track record of turning terabytes of data into automated training pipelines and more reliable production workloads. Beyond research, he brings a pragmatic interest in quant and investment strategies, reflecting a broader appetite for systems that optimize both performance and value.
10 years of coding experience
2 years of employment as a software developer
Visiting Scholar, Computer Science, Visiting Scholar, Computer Science at University of California, BerkeleyBachelor's degree, Software Engineering, 89/100, Bachelor's degree, Software Engineering, 89/100 at Tsinghua UniversityHigh School Diploma, High School Diploma at Wuhan Foreign Languages SchoolMaster's degree, Computer Science, 3.83/4.0, Master's degree, Computer Science, 3.83/4.0 at Carnegie Mellon UniversityExchange Student, Computer Science, 4.0/4.0, Exchange Student, Computer Science, 4.0/4.0 at The University of Texas at Austin
Github Skills (22)
kubernetes10
docker10
kubeflow10
dockers10
mlops10
go10
kubernetes-pods10
cgroups10
api-design9
software-design9
apim9
python9
system-design9
sys9
api9
Programming languages (10)
Github contributions (5)
koordinator-sh/koordinator
May 2022 - Aug 2022
A QoS-based scheduling system brings optimal layout and status to workloads such as microservices, web services, big data jobs, AI jobs, etc.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:32 reviews, 17 commits, 17 PRs in 2 months
Contributions summary:Zhengyu primarily contributed to the runtime proxy component of the Koordinator project, implementing and modifying APIs and interfaces related to container resource management. They added features like hooks for pre- and post-container operations, and container ID integration. Moreover, the user worked on ensuring proper cgroup parent configurations for Docker and systemd integrations, along with fixing PLEG-related cgroup path issues. These changes suggest a strong focus on runtime environment integration and management.
colocationcost-optimizationschedulingorchestrationworkloads
kubeflow/trainer
Mar 2022 - Jul 2022
Distributed ML Training and Fine-Tuning on Kubernetes
Role in this project:MLOps Engineer
MLOps EngineerContributions:6 reviews, 6 commits, 9 PRs in 4 months
Contributions summary:Zhengyu primarily contributed to the improvement and maintenance of Kubeflow's training infrastructure. Their commits focused on modifying Kubernetes resource configurations like MPIJobs and TFJobs, including bug fixes related to gang scheduling and restart policies. They also updated job status handling for PyTorch and XGBoost jobs, suggesting a focus on monitoring, logging, and overall operational efficiency within the training environment.
xgboostkubernetesmachine-learningtrainingai
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