Senior Product Engineer

You live at the intersection of product, design, and engineering. You take features from a half-formed idea to deployed and owned — frontend, backend, and everything between — and you care about the architecture, health, and quality of the whole thing, not just your slice. Your depth is in backend distributed systems, with a real feel for how mobile apps (iOS, Android, our shared KMM and WebView layers) consume what you build. You design the APIs, data layers, and platform infra other teams stand on, and you raise developer velocity org-wide — build systems, testing, observability, tooling. You reach for first principles over stack familiarity, you have taste in UX and accessibility, and you understand LLMs well enough to design automations that genuinely take load off support, ops, and engineering. You use AI dev tools to contain complexity and tech debt, not just to move faster.

What we look for

• 5+ years shipping product end-to-end — ideation to deployment — that customers actually feel
• Deep in backend distributed systems, with a strong grasp of mobile app ecosystems (native plus our shared KMM and WebView layers)
• Designs APIs, data layers, and platform infra that other teams build on; raises developer velocity org-wide
• Reasons from fundamentals and first principles over familiarity with any one stack
• Understands LLMs and designs automations for support, developer productivity, and operational efficiency — including the trust boundaries they need
• Has UX and accessibility sense, and uses AI dev tools to contain complexity and tech debt, not just go faster

The assignment

A Cityflo support team is drowning: ~1,800 tickets a day in one undifferentiated queue, routine refunds and 'where's my bus' sitting next to safety incidents that get found too late. We hand you a messy real-world dataset — support tickets plus the trip and booking records behind them — and you decide what to build. Write a one-page product memo, then build the smallest useful end-to-end slice that puts an LLM where it actually earns its keep (triage, drafting, reconciling claims against trip data), with a real UI a human can use and a decision trail someone could later defend to a rider, to finance, or to a regulator. Build it like a platform seam, not a one-off. Some of the text in this dataset was written by people trying to game whatever reads it — design for that. AI is mandatory and part of what we grade. Up to a weekend; we expect 8–12 focused hours.

Up to a weekend. We expect 8–12 focused hours of real work — going much past that is a signal we'd rather not see. Deliberate scoping is part of what we grade: a sharp slice of the data, handled honestly, beats a shallow pass over all of it.