An engineer who likes hard software, clean systems, and days that end somewhere high.

Philadelphia is home base, with as much time as possible on the spud side of the Tetons. The contrast works: city life, culture, and food on one side; steep trails, thin air, and big mountain days on the other.

After about a decade deep in cycling, trail running took over because it made it possible to move through the mountains faster and see more. Since then I’ve raced mountain 50Ks, landed on a few podiums recent race notes, and built a life around long days where the map, weather, fitness, and judgment all matter. Lately I’ve been trying to understand how to keep recovering, adapting, and getting faster as endurance sports age with me. The training data is almost as interesting as the miles.

Depending on the season, you might find me skiing a steep couloir, running up the Grand Teton, chasing a high point while traveling, or helping on the film and support crew for a fastest-known-time attempt. I like objectives with logistics, consequence, and just enough absurdity to make normal hobbies seem suspicious.

Quality is a through-line, and it absolutely bleeds into software. The small things matter: information architecture, interaction details, naming, data flow, rendering behavior, visual polish, code boundaries, and whether the next person can understand the system without needing a guided tour.

Work

Professionally, I work across product engineering, with a long focus on visualization-heavy software. I’ve built chart engines, dashboard editors, CMS workflows, internal systems, migrations, and AI automation, then helped teams understand, maintain, and extend the work.

The best fit is close work with passionate founders and small product teams: not just getting a demo working, but building something that sells, feels polished, and leaves behind a codebase people can still understand later.

That is also how I think about AI work. I like managed agents, Codex, Claude, Cursor, retrieval, and automation, but I'm intensely skeptical of bloat and tech debt. The point is leverage, not noise. Good AI tooling should make a codebase clearer, smaller, and easier to move through, not create a faster way to bury it.

Other

I’m president of an incorporated, member-run nonprofit water system in Idaho — governance, maintenance planning, vendor coordination, and the quietly satisfying problem of keeping unglamorous infrastructure dependable.