Research Staff, Voice AI Foundations [Remote]

Australia- Melbourne · Full-time · Remote · Posted Oct 10, 2026

At a glance

As a Member of the Research Staff at Deepgram, you will pioneer the development of Latent Space Models (LSMs) to solve fundamental data, scale, and cost challenges in voice AI. Your research focuses on advancing real-time APIs for speech-to-text, text-to-speech, and building production-grade voice agents at scale.

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.
Develop embedding systems that cleanly factorize the codec latent space into interpretable dimensions of speaker, content, style, environment, and channel effects.
Leverage latent recombination to generate synthetic audio data at previously impossible scales, unlocking joint model and data scaling paradigms for audio.
Train multimodal speech-to-speech systems that can understand any human and produce empathic, human-like responses.
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.

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.