I’m a product engineer with deep iOS roots. Most of my career has been spent building consumer apps, but the work that keeps pulling me in is a little broader than any one platform.
I like the messy middle where user experience, engineering constraints, and team speed all pull on each other. Sometimes that means native iOS architecture and performance work. Sometimes it means subscription systems, telemetry, internal tools, or AI-assisted workflows that make product development move with more confidence.
For the last few years I’ve been working on subscription and growth surfaces at Wolt (DoorDash) in Tokyo, owning parts of the funnel end-to-end from cart and checkout entry points through signup and retention.
The work is rarely about building a single feature. It’s about breaking problems into experiments, removing friction, and shipping changes that measurably improve conversion, order volume, and retention.
Before that, I spent four years at SmartNews shipping iOS features to millions of users, working across onboarding, feed, article rendering, and server-driven UI.
Before that, five years at Bodybuilding.com, where I grew into the role by taking ownership early, shipping full features, and learning what 60fps actually feels like under load.
Across all of that, the pattern is the same: I tend to end up close to the surface where product decisions and engineering reality meet, simplifying flows, making systems behave predictably, and getting changes shipped and measured.
Increasingly, I’m interested in the systems behind product development itself: observability, experimentation infrastructure, developer tooling, and practical AI-assisted workflows that help teams move faster with more confidence.
It’s less about “learning AI” as a separate lane and more about understanding where these tools actually change implementation work, evaluation, feedback loops, and the shape of small internal systems.
What I’m good at
Owning consumer features end-to-end. Reading a funnel and finding the four pixels of friction nobody’s looking at. Breaking large efforts into shippable experiments. Evolving iOS codebases without slowing the team. Building telemetry, internal tools, and platform foundations people trust enough to act on. Using AI tools as part of implementation work without pretending they replace engineering judgment.
What I’m learning
How to build AI systems that behave reliably in real products, not just demos. Orchestration, evaluation, and where the edges actually are. Also, how to keep using iOS depth as a foundation without letting it become the whole boundary of the work.
Outside the editor
I live in Fujisawa, near the ocean south of Tokyo, in a house designed around airtightness, solar, and a V2H-equipped EV.
Two young daughters. A very patient wife. Cycling year-round, snowboarding in winter, and re-learning to skateboard. Still anticipating the day I finally start surfing.