Ziming Mao is a PhD student and researcher at UC Berkeley specializing in distributed systems and cloud-native infrastructure, with about six years of experience spanning academic research and industry internships. He has worked on memory disaggregation, distributed caching, and serving/training on spot instances under advisors like Ion Stoica and Scott Shenker, and interned at Databricks. Ziming contributes to production-focused open source—most notably backend and spot-instance reliability features for SkyPilot, helping optimize GPU availability and rolling updates across multi-cloud environments. Comfortable bridging research and engineering, he combines rigorous CS training from Yale with hands-on DevOps and backend work to move prototypes toward resilient, cost-efficient deployments. An aside that’s not obvious: his background spans both NLP research and systems work, giving him a rare cross-disciplinary perspective on scalable ML systems.
6 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
Bachelor's degree, Computer Science (B.S.) and Philosophy (B.A.), Bachelor's degree, Computer Science (B.S.) and Philosophy (B.A.) at Yale University
High School Diploma, Science and Math Talent Program, High School Diploma, Science and Math Talent Program at Hwa Chong Institution
SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 15+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
Role in this project:
Backend & DevOps Engineer
Contributions:431 reviews, 56 PRs, 519 pushes in 1 year 9 months
Contributions summary:Ziming primarily contributed to the backend of the SkyPilot project, focusing on the "Spot" functionality for managing and optimizing cloud resources. Their work involved adding features like event callbacks for notifications and implementing a spot dashboard with delays for initialization. Furthermore, the user contributed to improving the system's reliability by adding code for automatic recovery and implementing a rolling update feature. They also worked on supporting multiple resources.
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