Software engineer specializing in search systems, algorithms, performance optimization, and machine learning infrastructure. Over nearly a decade of professional experience, I've built a track record of delivering reliable, maintainable, and time-tested software, backed by rigorous, deterministic testing. My pull requests across the companies listed on my LinkedIn profile (including a unicorn and Big Tech companies) reflect this engineering discipline. I also believe in leaving a little room for safe experimentation—a small percentage of the time across a high volume (thousands) of commits over the past decade, with experimentation typically front-loaded when exploring new technologies or problem domains. However, maintaining low revisions per PR remains important, as it has in my career in Big Tech (1.2-1.3). Regardless of code and PRs, my strength is in problem solving, and this has been demonstrated in projects, interviews (e.g. Senior Software Engineer @ Google), and more.
Founder of Software.Land, building systems for semantic knowledge representation, search, and learning progression.
Source-available search engine built with TypeScript, featuring positional inverted indexes, compact typed-array representations, block-level pruning, and ranking optimizations.
Contributions to the C/C++ LLM inference ecosystem:
- #31165 — Fix dangling pointer in multimodal embedding batch management
- #29060 — HTTP 400 handling for invalid embedding server requests
- #27920 — Qwen3.5 embedding model conversion support
- #16541 — Raw embedding CLI output support
After some earlier experimentation, I'm getting closer to no longer being a novice in LLM inference engineering. Larger PRs coming up.
Search and information retrieval · Algorithms and data structures · Distributed systems · LLM inference · Knowledge graphs