{"schemaVersion":"jobsearcher.job.v1","id":"dd01d75e56d64b0385e3154d","url":"https://jobsearcher.com/jobs/dd01d75e56d64b0385e3154d","canonicalUrl":"https://jobsearcher.com/jobs/dd01d75e56d64b0385e3154d","title":"Machine Learning Engineer - On-Device Speech Recognition","description":"Machine Learning Engineer - On-Device Speech Recognition$200,000 - $300,000San Francisco, hybrid (3x per week)Full time / PermanentThis 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 doDesign and train lightweight on-device ASR models (e.g. Streaming Transducer, CTC) that run efficiently on mobile and embedded hardwareCompress and optimize models using quantization, pruning, and knowledge distillationClean, align, and augment multilingual speech data; handle low-resource languages and noisy real-world conditionsWork closely with engineering teams to convert and deploy models into productionWhat \"great\" looks likeYou've trained or fine-tuned ASR models at production scale, not just in research settingsYou know at least one major ASR framework deeply (Wenet, Espnet, Icefall/K2, or Zipformer) and understand how they actually work at a structural levelYou've deployed on-device or offline ASR models and solved the messy problems that come with real hardware constraintsYou've done hands-on post-training quantization and know how to recover accuracy when it degradesMaster's or PhD in Computer Science, Signal Processing, or similar, and 3–5 years in speech algorithmsBonus: published research at ICASSP or Interspeech, experience with Zipformer / Paraformer / SenseVoice, or knowledge distillation from large speech models to compact ones.Why joinProfitable, fast-moving company. Your work ships and gets used by over a million peopleReal ownership of the on-device speech stack, not one task on a large team's backlogHybrid San Francisco team building both hardware and AI systems in parallelMeaningful datasets and global product scale to test and prove your workClear growth toward senior technical leadership as the audio function expands","company":"Deeprecai","rawCompany":"deeprecai","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-05-03T08:42:08.135Z","occupations":[{"code":"29-1127.00","title":"Speech-Language Pathologists","slug":"speech-language-pathologists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541930","title":"Translation and Interpretation Services","slug":"translation-and-interpretation-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - On-Device Speech Recognition","description":"Machine Learning Engineer - On-Device Speech Recognition$200,000 - $300,000San Francisco, hybrid (3x per week)Full time / PermanentThis 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 doDesign and train lightweight on-device ASR models (e.g. Streaming Transducer, CTC) that run efficiently on mobile and embedded hardwareCompress and optimize models using quantization, pruning, and knowledge distillationClean, align, and augment multilingual speech data; handle low-resource languages and noisy real-world conditionsWork closely with engineering teams to convert and deploy models into productionWhat \"great\" looks likeYou've trained or fine-tuned ASR models at production scale, not just in research settingsYou know at least one major ASR framework deeply (Wenet, Espnet, Icefall/K2, or Zipformer) and understand how they actually work at a structural levelYou've deployed on-device or offline ASR models and solved the messy problems that come with real hardware constraintsYou've done hands-on post-training quantization and know how to recover accuracy when it degradesMaster's or PhD in Computer Science, Signal Processing, or similar, and 3–5 years in speech algorithmsBonus: published research at ICASSP or Interspeech, experience with Zipformer / Paraformer / SenseVoice, or knowledge distillation from large speech models to compact ones.Why joinProfitable, fast-moving company. Your work ships and gets used by over a million peopleReal ownership of the on-device speech stack, not one task on a large team's backlogHybrid San Francisco team building both hardware and AI systems in parallelMeaningful datasets and global product scale to test and prove your workClear growth toward senior technical leadership as the audio function expands","datePosted":"2026-05-03T08:42:08.135Z","dateModified":"2026-05-03T08:42:08.135Z","hiringOrganization":{"@type":"Organization","name":"Deeprecai","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"dd01d75e56d64b0385e3154d"},"url":"https://jobsearcher.com/jobs/dd01d75e56d64b0385e3154d"}}