Research Staff, Voice AI Foundations - Remote

Deepgram is a platform providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and production-grade voice agents. As a Member of the Research Staff, the role focuses on developing Latent Space Models (LSMs) to address data, scale, and cost challenges in building voice AI. The company expects an AI-first mindset, with active use of and experimentation with advanced AI tools.

Responsibilities

Pioneer the development of Latent Space Models (LSMs) for robust, contextualized voice AI; Build next-generation neural audio codecs with extreme low bit-rate compression and high fidelity reconstruction; Pioneer steerable generative models that synthesize the full diversity of human speech from codec latent representations; Develop embedding systems that factorize the codec latent space into interpretable dimensions (speaker, content, style, environment, channel effects) and enable latent recombination; Generate synthetic audio data at large scales and train multimodal speech-to-speech systems; Design model architectures, training schemes, and inference algorithms adapted for bare-metal hardware to enable cost-efficient training on billion-hour datasets and real-time inference

Requirements

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, deriving novel mathematical formulations and implementing them efficiently; Demonstrated ability to build data pipelines that 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