Research Staff, Voice AI Foundations

The Role

As a Member of the Research Staff, you will pioneer the development of Latent Space Models (LSMs), a new approach that aims to solve the fundamental data, scale, and cost challenges associated with building robust, contextualized voice AI. Your research will focus on solving one or more of the following problems:

• Build next-generation neural audio codecs that achieve extreme, low bit-rate compression and high fidelity reconstruction across a world-scale corpus of general audio.
• Pioneer steerable generative models that can synthesize the full diversity of human speech from the codec latent representation, from casual conversation to highly emotional expression to complex multi-speaker scenarios with environmental noise and overlapping speech.
• Develop embedding systems that cleanly factorize the codec latent space into interpretable dimensions of speaker, content, style, environment, and channel effects -- enabling precise control over each aspect and the ability to massively amplify an existing seed dataset through “latent recombination.”
• Leverage latent recombination to generate synthetic audio data at previously impossible scales, unlocking joint model and data scaling paradigms for audio.
• Design model architectures, training schemes, and inference algorithms that are adapted for hardware at the bare metal, enabling cost efficient training on billion-hour datasets and powering real-time inference for hundreds of millions of concurrent conversations.

Challenge

We are seeking researchers who:

• See "unsolved" problems as opportunities to pioneer entirely new approaches.
• Identify the one critical experiment that will validate or kill an idea in days, not months.
• Have the vision to scale successful proofs-of-concept 100x.
• Be obsessed with using AI to automate and amplify their own impact.

It's Important to Us

An ideal researcher, as evidenced by the recurring themes across these foundational papers, excels in five key areas: (1) Statistical & Mathematical Foundations, (2) Algorithmic Innovation & Implementation, (3) Data-Driven & Scalable Systems, (4) Hardware & Systems Understanding, and (5) Rigorous Experimental Design.