[Hiring] Research Staff, Voice AI Foundations @Deepgram

Role Description

As a Member of the Research Staff, you will pioneer the development of Latent Space Models (LSMs), a new approach that aims to solve the fundamental data, scale, and cost challenges associated with building robust, contextualized voice AI. Your research will focus on solving one or more of the following problems:

• 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.

Qualifications

• 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.

Requirements

• See "unsolved" problems as opportunities to pioneer entirely new approaches.
• Can identify the one critical experiment that will validate or kill an idea in days, not months.
• Have the vision to scale successful proofs-of-concept 100x.
• Are obsessed with using AI to automate and amplify your own impact.

Benefits

• Opportunity to work on cutting-edge voice AI technology.
• Collaborative and innovative work environment.
• Potential for significant impact in the field of AI.

Company Description

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency.