Research Staff, Voice AI Foundations

About the role

As a member of the research staff, you will pioneer the development of Latent Space Models to address fundamental data, scale, and cost challenges in voice AI. You will work on next-generation neural audio codecs, steerable generative models, and multimodal speech-to-speech systems. This role requires an AI-first mindset and the ability to adapt quickly to the rapid pace of AI innovation.

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 that factorize codec latent space into interpretable dimensions for precise control.
• 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 bare-metal hardware.

Required skills

• Statistical learning theory
• Foundation model architectures
• Multimodal learning
• Data pipelines
• Experimental design
• Speech AI
• Language AI

Qualifications

• Strong mathematical foundation in statistical learning theory
• Deep expertise in foundation model architectures
• Proven ability to bridge theory and practice
• Demonstrated ability to build data pipelines for massive datasets

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's voice-native foundation models are used by over 200,000 developers and 1,300 organizations.