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Deepgram is the leading platform for the Voice AI economy, providing real-time APIs for speech-to-text, text-to-speech, and production-grade voice agents at scale. The company has processed over 50,000 years of audio and transcribed more than 1 trillion words, serving 200,000+ developers and 1,300+ organizations including Twilio, Cloudflare, and Vapi.
As a Member of the Research Staff, you will pioneer the development of Latent Space Models (LSMs), a novel approach to solve fundamental data, scale, and cost challenges in building robust, contextualized voice AI. Your research will focus on one or more of these core problems:
• Build next-generation neural audio codecs achieving extreme, low bit-rate compression and high-fidelity reconstruction across world-scale audio corpora
• Pioneer steerable generative models synthesizing the full diversity of human speech from codec latent representations, including casual conversation, emotional expression, multi-speaker scenarios with environmental noise and overlapping speech
• Develop embedding systems that factorize codec latent space into interpretable dimensions (speaker, content, style, environment, channel effects) enabling precise control and dataset amplification through latent recombination
• Leverage latent recombination to generate synthetic audio at unprecedented scales, unlocking new joint model and data scaling paradigms
• Train multimodal speech-to-speech systems understanding any human regardless of demographics, state, or environment, producing empathic, human-like responses for conversational or task-oriented objectives
• Design model architectures, training schemes, and inference algorithms optimized for bare-metal hardware, enabling cost-efficient training on billion-hour datasets and real-time inference for hundreds of millions of concurrent conversations
The role demands obsession with unsolved problems, creativity in approach, and relentless drive toward elegant, scalable solutions. You will identify critical experiments that validate or kill ideas in days, scale successful proofs-of-concept 100x, and use AI to automate and amplify your own impact. Deepgram operates with an AI-first mindset where all team members actively use and experiment with advanced AI tools; change is rapid and you should expect your day-to-day work to evolve quickly.
REQUIREMENTS:
• Strong mathematical foundation in statistical learning theory, particularly self-supervised and multimodal learning
• Deep expertise in foundation model architectures and understanding 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 processing and curating massive datasets while maintaining quality and diversity
• Track record designing controlled experiments isolating the impact of architectural innovations and validating 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 advancing the state of the art in speech/language AI
Ideal candidates excel in five key areas: (1) Statistical & Mathematical Foundations—mastery of power-law analyses, novel loss derivations, and combining existing theories in novel ways; (2) Algorithmic Innovation & Implementation—creative solutions to known bottlenecks with clear prototypes and open-source contributions; (3) Data-Driven & Scalable Systems—emphasis on large-scale data pipelines and strategic staged training approaches; (4) Hardware & Systems Understanding—efficient implementations for modern accelerators with awareness of real-time and on-device constraints; (5) Rigorous Experimental Design—controlled comparisons, ablations, multifold evaluation metrics, and stress tests on edge cases.
