About the role
Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that "unmute" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth.
About
the Role At Mirelo, we’re pushing the limits of what generative audio can do, and our ability to innovate depends heavily on the quality of our underlying audio representations. In this role, you’ll work at the core of our modeling stack - designing, training, and evaluating neural audio codecs that directly shape the performance of our next-generation music and sound models. You’ll collaborate closely with the model team, experiment with both continuous and discrete representations, and build the evaluation tools that help us understand what actually moves the needle. Your work will sit at the foundation of building the best-sounding generative models in the world. Key
Responsibilities 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. Ideal Candidate Profile 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