Research Staff, Voice AI Foundations

Readable role content extracted into sections for faster review.

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. Endeavor to train multimodal speech-to-speech systems that can 1) understand any human irrespective of their demographics, state, or environment and 2) produce empathic, human-like responses that achieve conversational or task-oriented objectives.
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.

See "unsolved" problems as opportunities to pioneer entirely new approaches
Can 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
Are obsessed with using AI to automate and amplify your own impact

Strong mathematical foundation in statistical learning theory, particularly in areas relevant to self-supervised and multimodal learning
Deep expertise in foundation model architectures, with an understanding of how to scale training across multiple modalities
Proven ability to bridge theory and practice—someone who can both derive novel mathematical formulations and implement them efficiently
Demonstrated ability to build data pipelines that can process and curate massive datasets while maintaining quality and diversity
Track record of designing controlled experiments that isolate the impact of architectural innovations and validate theoretical insights
Experience optimizing models for real-world deployment, including knowledge of hardware constraints and efficiency techniques
History of open-source contributions or research publications that have advanced the state of the art in speech/language AI

A solid grounding in theoretical and statistical principles
A talent for proposing and validating new algorithmic solutions
The capacity to orchestrate data pipelines that scale and reflect real-world diversity
Awareness of hardware constraints and system-level trade-offs for efficiency
Thorough and transparent experimental practices

Robust Speech Recognition via Large-Scale Weak Supervision (arXiv:2212.04356)
Moshi: a speech-text foundation model for real-time dialogue (arXiv:2410.00037)
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (arXiv:2403.03206)
Scaling Laws for Neural Language Models (arXiv:2001.08361)
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data (arXiv:2402.08093)
In-Datacenter Performance Analysis of a Tensor Processing Unit (arXiv:1704.04760)
Neural Discrete Representation Learning (arXiv:1711.00937)
SoundStream: An End-to-End Neural Audio Codec (arXiv:2107.03312)
Finite Scalar Quantization: VQ-VAE Made Simple (arXiv:2309.15505)
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (arXiv:2404.14219)
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality (arXiv:2405.21060)