Research Staff, Voice AI Foundations

About the role

Deepgram is seeking a member of the research staff to pioneer the development of Latent Space Models to address fundamental data, scale, and cost challenges in voice AI. You will focus on building next-generation neural audio codecs, steerable generative models, and embedding systems to enable universal human-machine interaction. This role requires an AI-first mindset and the ability to bridge theoretical research with efficient, scalable implementation.

Responsibilities

• Build next-generation neural audio codecs for extreme, low bit-rate compression and high fidelity reconstruction.
• Pioneer steerable generative models capable of synthesizing diverse human speech and emotional expression.
• Develop embedding systems to factorize codec latent space for precise control over speaker, content, and style.
• Leverage latent recombination to generate synthetic audio data at scale.
• Train multimodal speech-to-speech systems that produce empathic, human-like responses.
• Design model architectures, training schemes, and inference algorithms optimized for hardware efficiency.

Required skills

• Statistical learning theory
• Foundation model architectures
• Multimodal learning
• Neural audio codecs
• Generative models
• Data pipeline development
• Experimental design
• Model optimization

Qualifications

• Strong mathematical foundation in statistical learning theory
• Deep expertise in foundation model architectures
• Proven ability to bridge theory and practice
• History of open-source contributions or research publications

About the Company

Deepgram is a platform for the Voice AI economy, providing real-time APIs for speech-to-text and text-to-speech, and building production-grade voice agents. The company has processed over 50,000 years of audio and is backed by global investors.