Research Staff, Voice AI Foundations

About the job

Responsibilities

• Develop next-generation neural audio codecs for extreme low bit-rate compression and high fidelity reconstruction.
• Pioneer steerable generative models for synthesizing diverse human speech from latent representations.
• Develop embedding systems to factorize codec latent space into interpretable dimensions for precise control.
• Leverage latent recombination to generate synthetic audio data at scale for joint model and data scaling.
• Train multimodal speech-to-speech systems for understanding and producing human-like responses.
• Design model architectures, training schemes, and inference algorithms optimized for hardware efficiency and real-time inference.

Requirements

• Strong mathematical foundation in statistical learning theory, especially self-supervised and multimodal learning.
• Deep expertise in foundation model architectures and scaling training across modalities.
• Proven ability to bridge theory and practice, deriving novel mathematical formulations and implementing them efficiently.
• Demonstrated ability to build data pipelines for processing and curating massive datasets.
• Track record of designing controlled experiments to validate architectural innovations and theoretical insights.
• Experience optimizing models for real-world deployment, considering hardware constraints and efficiency.