About The Role

ABOUT THE ROLE

You'll own the transcription pipeline end-to-end at an early-stage ambient intelligence startup — a cloud-based ASR system with a narrowly scoped on-device component. As one of the first US engineering hires, you'll work hands-on with product and leadership to build, tune, and ship pipeline improvements that directly shape the core product experience.

WHAT YOU'LL DO

Build and iterate on the cloud-based ASR pipeline, from audio capture through post-processing, in production at scale.
Own ASR quality and reliability end-to-end, shipping measurable improvements across latency, small-word accuracy, and voice-print reliability.
Work across data, training/fine-tuning, evaluation, and deployment to translate product feedback into shipped pipeline changes.
Collaborate with overseas R&D, hardware, and supply-chain teams across time zones.
Partner with a product-focused backend engineer on shared pipeline surfaces.
Operate with minimal specification — turning lightweight asks into concrete, production-ready improvements.

WHAT WE'RE LOOKING FOR

3+ years building and tuning transcription/ASR pipelines end-to-end in production, primarily in cloud-based settings.
Demonstrated ownership of production ASR systems across the full lifecycle: data preparation, model training/fine-tuning, evaluation, and deployment.
Experience building and optimizing latency-sensitive or streaming audio/ASR pipelines.
Track record of shipping pipeline improvements from design through deployment based on real production usage data.
Comfort making latency, accuracy, and reliability tradeoffs based on genuine user feedback — not just benchmark scores.
Prior experience at an early-stage or founding-team engineering environment with minimal specifications and small teams.
On-device or embedded ML experience (e.g. Core ML, TensorFlow Lite, or similar frameworks) is a plus.
Background in wearable, hardware, or robotics device products is a plus.
Experience at an AI-native consumer application focused on transcription or audio is a plus.
Experience building agent or LLM-based product features (tool use, memory, retrieval) is a plus.