Systems and ML infra engineer working on kernels, inference, performance, and end-to-end software.
I build low-level systems when the abstractions need to be understood from first principles, and I build product and infrastructure layers when those systems need to be shipped, measured, and debugged in the real world. Current work ranges from RISC-V operating systems and AI accelerator kernel optimization to edge-cloud inference pipelines and AWS-backed experiment runners.
The through-line in the work is systems discipline: understand the runtime, make state visible, and keep the feedback loop tight enough that performance and correctness are both inspectable.
The portfolio is intentionally organized like a workstation because the projects here are easier to evaluate through interfaces, traces, and technical notes than through generic marketing copy.