Applied ML Engineer, Speech

About the role

About us

Our mission is to reinvent the way people learn, starting with language.

Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages.

Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana.

About this role

We are looking for an experienced Machine Learning Engineer to join our team and help develop cutting-edge speech recognition models that help teach language fluency. In this role you will take ownership of the end-to-end modeling pipeline, from training and experimentation to deployment and monitoring. You will also work closely with Product teams to design innovative learning experiences and measure the efficacy of production models as they affect our end users. We are a small, dynamic team where you will contribute as a developer and thought partner on team projects like ASR, assessment, pronunciation, content personalization, and much more. This is an incredibly exciting time to join an ML team designing a personalized learning experience that will revolutionize language learning for millions of learners worldwide — come join us!

What you'll be doing

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Training and deploying ASR models end-to-end, including monitoring, performance tracking, and retraining

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Improving the pronunciation model that provides precise feedback, and make it more central to our learning app

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Creating metrics to measure ASR performance across tasks and languages

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Expanding our ASR systems to new languages and markets

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Building and maintaining data infrastructure such as training/evaluation datasets and labeling pipelines

What we're looking for

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Extensive experience training large models on GPUs and deploying custom deep learning models

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Proficiency in Python and common Deep Learning frameworks like PyTorch

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Demonstrated experience owning ML pipelines end to end, from POC to production

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Strong communication skills and the ability to explain complex ML concepts to non-technical stakeholders