Clera
ASR Engineer
About this role
Join an early-stage ambient intelligence startup as one of the first US engineering hires to design and optimize a production cloud-based ASR pipeline. You'll own transcription quality end-to-end, working directly with product leadership to ship latency, accuracy, and reliability improvements while collaborating across hardware and R&D teams.
What you'll do
- Build and iterate on cloud-based ASR pipeline from audio capture through post-processing at production scale
- Own ASR quality and reliability end-to-end, shipping measurable improvements in latency, small-word accuracy, and voice-print reliability
- Execute across data preparation, model training/fine-tuning, evaluation, and deployment to translate product feedback into shipped changes
- Debug transcription quality issues in production and iterate based on real usage data
- Collaborate with hardware and R&D teams across time zones and partner with Product Engineer on shared backend surfaces
- Operate autonomously in early-stage environment, turning lightweight product asks into concrete, production-ready improvements
What they're looking for
- ASR/transcription pipeline development and optimization
- Cloud-based audio processing and streaming systems
- Model training, fine-tuning, and evaluation
- Production machine learning deployment and monitoring
- Latency-sensitive system design and debugging
- On-device ML frameworks (Core ML, TensorFlow Lite preferred)
- Audio feature engineering and signal processing
- Familiarity with LLM/agent-based product features
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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
- Describe a time you shipped a measurable improvement to an ASR system in production—what was the metric, and how did you validate the change?
- How would you approach debugging poor small-word accuracy or voice-print reliability issues in a live transcription pipeline?