| Location | San Francisco, California |
You'll own the transcription pipeline end-to-end at an early-stage ambient intelligence startup — one of the company's first US engineering hires. Working directly with the GM and Head of Product, you'll build, tune, and ship improvements to a cloud-based ASR system with a narrowly scoped on-device component, making real tradeoffs between latency, accuracy, and reliability as the product evolves.
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 in 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 hardware and R&D teams across time zones.
Partner with a Product Engineer on shared backend and pipeline surfaces.
Operate with minimal specification, turning lightweight asks into concrete, production-ready improvements.
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 and ongoing iteration based on real usage data.
Experience debugging transcription quality issues in production (small-word accuracy, voice-print reliability, latency).
Comfort in early-stage or founding engineering environments with minimal specs and small teams.
On-device or embedded ML experience (Core ML, TensorFlow Lite, or similar frameworks) is a strong plus.
Prior experience building wearable, hardware, or robotics device products is a plus.
Background at AI-native consumer applications focused on transcription or audio is a plus.
Experience building agent or LLM-based product features (tool use, memory, retrieval) is a plus.
You care how transcription feels to use, not just how it benchmarks.
Hybrid (3 days/week in office) — San Francisco Bay Area, California. Visa sponsorship is not available.