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Company Description
Neuphonic is building the future of on-device voice AI.
We develop ultra-low-latency neural text-to-speech systems that enable super-realistic, human-like speech directly on devices. Our focus is on building efficient generative audio models 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 are making natural conversational AI possible on phones, embedded devices, browsers, and edge systems. This opens the door to a new generation of voice-enabled applications where interacting with AI feels as natural and responsive as speaking with another person.
Neuphonic was founded in April 2024 and is backed by leading venture capital firms in Europe. Our customers include OEM handset manufacturers, chip manufacturers, and consumer AI companies building the next generation of voice-enabled products.
Our vision is a world where voice becomes the most natural interface for AI, enabling seamless, intuitive interactions that are accessible to everyone.
To understand the technology you would be working on, please review our Hugging Face and GitHub repositories, as they will 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