Tooling and systems work for reasoning about remote GPU performance, virtualization, and infrastructure behavior in ML-oriented cloud environments.
This project reflects my work at Google on infrastructure and performance questions around GPU-accelerated machine learning. The emphasis was on building practical tools that help engineers reason about latency, bandwidth, and the impact of remote execution in cloud settings.
It combined systems research instincts with implementation work in C++, especially around simulation quality and developer workflows.