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Clera

Product Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join an early-stage AI consumer startup as a generalist Product Engineer, owning the full-stack app experience across iOS, Apple Watch, and Mac. You'll ship features end-to-end with minimal specification, collaborating directly with product leadership and integrating LLM/agent capabilities into the consumer experience.

What you'll do

  • Own and ship features across iOS, watchOS, and Mac platforms end-to-end
  • Build user-facing improvements addressing wearable reliability, cross-platform detection, and agent responsiveness
  • Integrate agent and LLM-based features including tool use, memory, and retrieval into the app
  • Move fluidly between UX, UI, backend, and AI/ML based on feature requirements
  • Collaborate with ASR Engineer on shared backend and pipeline surfaces
  • Iterate features based on real user feedback without waiting for formal specifications

What they're looking for

  • Swift and SwiftUI for iOS and watchOS
  • TypeScript and React for web/desktop development
  • Electron for Mac client development
  • Backend API and service design
  • LLM and agent integration
  • Full-stack product thinking and tradeoff decisions
  • Real-time or streaming systems (audio processing, data pipelines)
  • UX/UI design and implementation

Benefits

  • Hybrid work (3 days/week in office)
  • Early-stage startup equity opportunity
  • Direct collaboration with product leadership
  • Broad technical ownership and autonomy
  • San Francisco Bay Area location
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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 product feature you shipped end-to-end across multiple platforms—what were the key tradeoffs you made?
  • Describe your experience integrating LLM or agent capabilities into a production app. What were the main challenges?
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