Machine Learning Researcher at Neuphonic

Company

Neuphonic is building the future of on-device voice AI.

We develop ultra-low-latency voice models that enable super-realistic, human-like speech directly on devices. Our focus is on building efficient generative audio models and infrastructure that can run on CPU-constrained hardware, enabling real-time voice interaction without relying on large cloud infrastructure.

By dramatically reducing latency and compute requirements, we're making natural conversational AI possible on phones, embedded devices, browsers, and edge systems. This requires building high-performance systems that bridge machine learning, real-time audio processing, and distributed infrastructure.

Neuphonic was founded in April 2024 and has raised significant funding from leading European investors. We work with global enterprises across hardware and consumer AI that are building the next generation of voice-enabled products. Our open source models have been downloaded over 4 million times and earned over 6,500 GitHub stars since launch.

Our vision is a world where voice becomes the most natural interface for AI, enabling seamless, intuitive interactions that are accessible to everyone.

Please review our Hugging Face and GitHub repos before applying, as they’ll be part of the interview discussion.


Role

We are looking for a Machine Learning Engineer to help advance the state of the art in speech synthesis.

You will work on research and development across the full speech pipeline — from model architecture and training to dataset design and production deployment. The role combines applied research with real-world engineering, working closely with a small team pushing the boundaries of real-time speech systems.

We are particularly interested in candidates with experience in text-to-speech systems, or multimodal machine learning involving speech and audio.

Your work will include:

This role is best suited to candidates who have worked on research-grade machine learning models, rather than purely application-level ML systems.

You have

In addition, you should have experience in one of the following areas:


Benefits

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