Research Staff – Voice AI Foundations

• Develop next-generation neural audio codecs with extreme low-bit-rate compression and high-fidelity reconstruction
• Pioneer steerable generative models for diverse human speech, including emotional, multi-speaker, noisy, and overlapping-speech scenarios
• Develop embedding systems that factorize latent representations into speaker, content, style, environment, and channel dimensions
• Use latent recombination to generate synthetic audio data at previously impossible scales
• Train multimodal speech-to-speech systems for robust understanding and empathic human-like responses
• Design model architectures, training schemes, and inference algorithms optimized for bare-metal hardware
• Enable cost-efficient training on billion-hour datasets and real-time inference for hundreds of millions of concurrent conversations
• Conduct rigorous controlled experiments, ablations, evaluations, stress tests, and edge-case analyses
• Contribute to foundational research advancing voice AI, speech, and language modeling

• Strong mathematical foundation in statistical learning theory, particularly self-supervised and multimodal learning
• Deep expertise in foundation model architectures and scaling training across multiple modalities
• Ability to derive novel mathematical formulations and implement them efficiently
• Demonstrated ability to build and curate massive datasets while maintaining quality and diversity
• Track record of designing controlled experiments to validate architectural innovations and theoretical insights
• Experience optimizing models for real-world deployment, including hardware constraints and efficiency techniques
• History of open-source contributions or research publications advancing speech/language AI
• Comfort actively using and experimenting with advanced AI tools
• Ability to identify critical experiments that validate or disprove ideas quickly
• Vision to scale successful proofs-of-concept 100x
• Experience or knowledge relevant to neural audio codecs, generative models, embedding systems, latent representations, multimodal speech-to-speech systems, model architectures, training schemes, inference algorithms, and hardware-efficient deployment

• Remote work arrangement
• AI-first work environment with encouragement to use and experiment with advanced AI tools
• Opportunity to work on transformative voice AI research at scale
• Open-source contribution and research publication opportunities
• AI Notetaker interview recording can be declined without affecting candidacy