Machine Learning Engineer - On-Device Speech Recognition

Machine Learning Engineer - On-Device Speech Recognition

$200,000 - $300,000

San Francisco, hybrid (3x per week)

Full time / Permanent

This company builds AI-powered tools that help professionals capture and use what's said in the real world of work - meetings, conversations, voice notes. It's profitable, bootstrapped, and growing fast: $250M revenue run rate in under three years, with over 1.5 million users globally.

The product is working. The next step is making the speech engine significantly better by making it smaller, faster, and more accurate across every device and language it runs on.

What you'll do

Design and train lightweight on-device ASR models (e.g. Streaming Transducer, CTC) that run efficiently on mobile and embedded hardware

Compress and optimize models using quantization, pruning, and knowledge distillation

Clean, align, and augment multilingual speech data; handle low-resource languages and noisy real-world conditions

Work closely with engineering teams to convert and deploy models into productionWhat "great" looks like

You've trained or fine-tuned ASR models at production scale, not just in research settings

You know at least one major ASR framework deeply (Wenet, Espnet, Icefall/K2, or Zipformer) and understand how they actually work at a structural level

You've deployed on-device or offline ASR models and solved the messy problems that come with real hardware constraints

You've done hands-on post-training quantization and know how to recover accuracy when it degrades

Master's or PhD in Computer Science, Signal Processing, or similar, and 3-5 years in speech algorithms

Bonus: published research at ICASSP or Interspeech, experience with Zipformer / Paraformer / SenseVoice, or knowledge distillation from large speech models to compact ones.

Why join

Profitable, fast-moving company. Your work ships and gets used by over a million people

Real ownership of the on-device speech stack, not one task on a large team's backlog

Hybrid San Francisco team building both hardware and AI systems in parallel

Meaningful datasets and global product scale to test and prove your work

Clear growth toward senior technical leadership as the audio function expands