Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models.
Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context of generative audio models (in the end, the ultimate goal is to build the best audio generative models)
Explore the trade-offs of continuous (as typically used for diffusion models) vs. discrete audio representations (as typically used for autoregressive models).
Develop benchmarking pipelines for codec evaluation
Conduct initial experiments with generative models to verify that a new candidate codec is actually useful for our downstream tasks.

Strong background in deep learning for audio: neural codecs, source separation, speech models, or generative audio systems
Specific hands-on experience in designing and training neural audio codecs
Solid understanding of audio signal processing fundamentals
Strong track record (research and/or open-source) in the field of audio ML
Nice to Have: Hands-on experience with generative audio models and good intuition of how the choice of the codec influences the training and performance of the generative model
Strong publication record (e.g., NeurIPS, ICML, ICLR, Interspeech, ICASSP, WASPAA)

Competitive compensation and equity
Strong packages that ensure you share in the success you help create.
True ownership from day one. You'll have genuine autonomy and responsibility. Your ideas and work will directly shape our product and company direction.
Join at a pivotal moment. We've secured fresh funding and are gaining traction
now is when your contributions can make a real difference to our success.
Build for the next generation of creators. Be part of the innovation that will transform how creators work and thrive.

Research Scientist – Audio Codec

Key skills