Summary
Zhiqiang Zang is a research scientist at Meta with a Ph.D. in Software Engineering and Systems from UT Austin and over a decade of experience building AI infrastructure and developer-facing tooling. His recent work focuses on LLM profiling, GPU performance observability, and holistic trace analysis for AI efficiency and debugging within Meta Superintelligence Labs. He has hands-on experience migrating large-scale analytics platforms, lowering false positives in static analysis tools, and accelerating training support across hardware vendors. Comfortable bridging deep research and production engineering, he has interned across industry labs (Uber, Fujitsu, NIO) and contributed to practical tooling like NilAway. Based in Vancouver, he brings a systems-first mindset to AI performance problems and a scholarly curiosity hinted at by his reflective GitHub bio.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Software Engineering and Systems, Doctor of Philosophy - PhD, Software Engineering and Systems at The University of Texas at Austin
Bachelor of Engineering - BE, Telecommunications Engineering, Bachelor of Engineering - BE, Telecommunications Engineering at Beijing University of Posts and Telecommunications