Product Engineer

This person owns app features end-to-end across Ambi's client surfaces, working directly with the GM/Head of Product rather than off a fully-specified backlog. They'll be making real product tradeoffs on the fly - this is a generalist role, not a narrow specialist: someone who can move fluidly between UX, UI, backend, and AI/ML depending on what the feature actually needs. They will work closely with China-based R&D, hardware, and supply-chain teams, with frequent cross-time-zone communication, and with Ambi's ASR Engineer on the shared backend and pipeline surfaces both roles touch. This role is built for someone who tried product management and felt too far from the actual tech to be satisfied, and who tried pure engineering and felt boxed into a narrow slice without enough ownership over what got built or why. What you'll do Success in the first 90 days looks like: shipping user-facing improvements that move the needle on issues already surfaced from early product use (watch tap-target reliability, cross-platform meeting detection, agent responsiveness), and doing it with minimal spec - turning a Slack thread or a one-line ask from the GM into shipped product. What we're looking for All-rounder range: Genuinely capable across UX, UI, backend, and AI/ML - not deep in one and dabbling in the rest; has shipped consumer product features end-to-end across at least two of: mobile, desktop, wearable, backend Apple platform depth: Proficient in Swift/SwiftUI for iOS and watchOS; comfortable with Electron and web technologies (TypeScript/React) for the Mac client Founding/early-stage engineering: Has been one of the first engineers somewhere before; comfortable with undefined specs, fast iteration, and shipping without a large team backing them up Product sense with technical depth: Wants the judgment call *and* the ability to go build it - makes UX and scope tradeoffs independently, then implements them directly rather than handing off to someone else AI/agent fluency: Comfortable building and integrating agent/LLM-based features (tool use, memory, retrieval) into the app experience Ownership mentality: Takes a feature from design through ship through iteration based on real user feedback, without waiting to be told the next step Nice to have Wearable, hardware, or robotics device experience Prior work at an AI-native consumer app (Otter.ai, Plaud, Rewind, Granola, or similar) Experience at a hardware-adjacent or hardware startup Comfortable being the all-rounder across web/mobile/infra that a 1-2 person eng team requires #J-18808-Ljbffr