projects

Selected ML systems and research projects, with emphasis on retrieval quality, ranking, grounding, and deployment trade-offs.

Selected work

ML projects with the messy parts left in: data, latency, evaluation, and deployment.

These are the projects I would talk through in an interview. Each one has a concrete technical problem, a measurable result, and a set of trade-offs that shaped the design.

Earlier work

Older projects stay here when they show a useful research thread, system constraint, or implementation habit.