Clera
Product Engineer
About this role
Join an early-stage startup as a generalist product engineer shipping consumer AI experiences across iOS, watchOS, and Mac. You'll own features end-to-end, make autonomous product decisions, and work directly with leadership to build an ambient intelligence product that spans mobile, wearable, and desktop platforms.
What you'll do
- Own and ship user-facing features across iOS, watchOS, and Mac platforms independently
- Build agent and LLM-based features including tool use, memory, and retrieval systems
- Move fluidly between UX, UI, backend, and AI/ML to complete features end-to-end
- Collaborate with shared backend engineer on APIs and services powering multiple platforms
- Iterate on features based on real user feedback and ship without extensive specification
- Work across time zones with hardware and R&D teams in China
What they're looking for
- Swift and SwiftUI (iOS and watchOS)
- TypeScript, React, and Electron (Mac/desktop)
- Backend service and API design
- Agent and LLM integration for production apps
- Full-stack product thinking across UX, UI, and backend
- Real-time or streaming systems (audio, live data)
- Independent decision-making and scope tradeoffs
- Early-stage startup product development
Benefits
- Equity stake given early-stage nature
- Direct collaboration with product leadership
- Hybrid arrangement with 3 days per week in office
- Work on ambient intelligence and wearable technology
- Autonomous ownership of features end-to-end
- Exposure to hardware, AI/ML, and consumer product development
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a consumer app feature you shipped end-to-end across multiple platforms. What were the key tradeoffs?
- Describe your experience integrating LLM or agent-based features into a production application. What were the biggest challenges?