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Clera

ASR Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Own the end-to-end cloud-based ASR transcription pipeline at an early-stage ambient intelligence startup. You'll build, tune, and deploy production improvements across latency, accuracy, and reliability while collaborating with product, hardware, and overseas R&D teams.

What you'll do

  • Build and iterate on the cloud-based ASR pipeline from audio capture through post-processing in production
  • Own ASR quality and reliability end-to-end, shipping measurable improvements in latency, small-word accuracy, and voice-print reliability
  • Work across data preparation, model training/fine-tuning, evaluation, and deployment to translate product feedback into shipped changes
  • Collaborate with overseas R&D, hardware, and supply-chain teams across time zones
  • Debug transcription quality issues and make latency/accuracy tradeoffs in live systems
  • Operate with minimal specification, turning lightweight asks into concrete production-ready improvements

What they're looking for

  • ASR/transcription pipeline development and tuning
  • Cloud-based audio processing and streaming systems
  • Model training and fine-tuning for speech recognition
  • Production deployment and monitoring
  • On-device ML frameworks (Core ML, TensorFlow Lite, or similar)
  • Audio quality debugging and latency optimization
  • Early-stage product development and autonomous shipping
  • Hardware and embedded systems integration

Benefits

  • Early-stage founding engineering environment with hands-on impact
  • Work directly with senior product and business leadership
  • Hybrid work arrangement (3 days/week in San Francisco office)
  • Opportunity to own mission-critical systems end-to-end
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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 transcription quality issue you debugged in production — what was wrong and how did you measure the fix?
  • Describe your experience building latency-sensitive ASR or streaming audio pipelines. What were the key tradeoffs you made?
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