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Clera

Founding Product Engineer

San FranciscoFrom $200kfulltimemidAdded today

About this role

Join an early-stage ambient intelligence startup as a Founding Product Engineer to build end-to-end consumer products across mobile, wearable, and desktop platforms. You'll own features from concept through launch, working directly with product leadership to ship AI-driven experiences with minimal specification and maximum autonomy.

What you'll do

  • Own feature development end-to-end across mobile, desktop, wearable, and AI agent/speech recognition pipelines
  • Collaborate directly with Head of Product to translate rough concepts into shipped features
  • Make independent technical trade-offs across UX, backend, and ML components based on feature requirements
  • Improve ASR latency and accuracy, wearable reliability, cross-platform detection, and agent responsiveness
  • Guide features from design through launch and iteration using user feedback
  • Partner with Product and Design to execute roadmap priorities

What they're looking for

  • Full-stack product engineering (frontend, backend, AI/ML)
  • Mobile development (iOS, Android, or cross-platform frameworks)
  • Real-time and low-latency streaming systems
  • Backend and API design
  • Strong product sense and independent decision-making
  • Speech, audio, or ASR pipeline experience
  • Agent or LLM feature development
  • WatchOS or wearable platform development

Benefits

  • Founding team equity package
  • Annual salary of $200,000 USD
  • Hybrid work arrangement in San Francisco
  • Early-stage startup environment with high ownership
  • Direct collaboration with product leadership
  • Work on cutting-edge AI-native consumer products
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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 feature you shipped end-to-end across multiple platforms. What trade-offs did you make and why?
  • Tell us about a time you built or optimized a real-time or low-latency system. What were the constraints and how did you approach them?
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