Research Staff, Voice AI Foundations

Summary

Deepgram is the leading voice AI platform for developers building speech-to-text, text-to-speech, and full speech-to-speech offerings. The Research Staff role focuses on pioneering the development of Latent Space Models to address challenges in building robust voice AI, including developing neural audio codecs and multimodal speech-to-speech systems.

Responsibilities

• 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

Skills

• 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

Qualifications

Must Haves

• 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