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Clera

Product Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join an early-stage ambient AI startup as a generalist Product Engineer owning the full app experience across iOS, Apple Watch, and Mac plus backend systems. You'll ship user-facing features quickly, integrate LLM-based capabilities, and make independent design tradeoffs while collaborating with hardware and R&D teams.

What you'll do

  • Own features end-to-end across iOS, watchOS, and Mac platforms with minimal specification
  • Build and ship user-facing improvements based on real product usage feedback
  • Move fluidly between UX, UI, backend, and AI/ML work as features require
  • Integrate agent and LLM-based features including tool use, memory, and retrieval into the app
  • Collaborate with ASR Engineer on shared backend and pipeline surfaces
  • Take features from design through launch and iteration based on user feedback

What they're looking for

  • Swift and SwiftUI for iOS and watchOS development
  • TypeScript, React, and Electron for desktop/Mac development
  • Backend API and shared service development
  • LLM and agent integration in production applications
  • Real-time or streaming systems (audio processing, live pipelines)
  • UX and UI design with independent tradeoff decision-making
  • Wearable and hardware product development
  • Cross-timezone collaboration and asynchronous communication

Benefits

  • Hybrid work arrangement (3 days per week in San Francisco office)
  • Opportunity to shape product direction at early-stage startup
  • Work directly with product leadership and hardware teams
  • Broad technical ownership across multiple platforms and domains
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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.

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Likely interview questions

  • Walk us through a consumer app feature you shipped end-to-end across multiple platforms—how did you handle the platform-specific tradeoffs?
  • Describe your experience integrating LLM or agent-based features into a production app. What were the biggest integration challenges?
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