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Research Staff, Voice AI Foundations

Deepgram· Research
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Remote$150k - $250k + equity + bonusFull-timePosted 11mo ago

About the role

Pioneer latent space modeling and scalable audio AI research to enable robust, context-aware voice AI through Latent Space Models (LSMs) and novel neural audio codecs, with a focus on data-efficient, multi-modal, and hardware-conscious research.

Responsibilities

  • Pioneer development of Latent Space Models (LSMs) to address data, scale, and cost challenges in voice AI.
  • Build next-generation neural audio codecs with extreme, low bit-rate compression and high-fidelity reconstruction across a world-scale corpus of general audio.
  • Pioneer steerable generative models capable of synthesizing diverse human speech across contexts (conversation, emotion, multi-speaker scenarios with noise and overlap).
  • Develop embedding systems that factorize codec latent space into interpretable dimensions (speaker, content, style, environment, channel effects) for latent recombination and data amplification.
  • Leverage latent recombination to generate synthetic audio data at unprecedented scales to enable joint model and data scaling; train multimodal speech-to-speech systems that understand any human and produce empathic, effective responses.
  • Design model architectures, training schemes, and inference algorithms optimized for hardware at bare metal to enable cost-efficient training on billion-hour datasets and real-time inference for hundreds of millions of concurrent conversations.

Qualifications

  • Strong mathematical foundation in statistical learning theory, especially self-supervised and multimodal learning.
  • Deep expertise in foundation model architectures and scalable training across modalities.
  • Ability to bridge theory and practice with implementable solutions.
  • Experience building data pipelines for massive datasets with quality and diversity.
  • Track record designing controlled experiments to validate architectural innovations and theoretical insights.
  • Experience optimizing models for real-world deployment with hardware awareness and efficiency techniques.
  • History of open-source contributions or publications advancing speech/language AI.

Skills

Required:Statistical learning theoryFoundation model architecturesData pipeline designExperimental designHardware-conscious optimization
Nice to have:Open-source contributions/publications
Last updated Oct 14, 2026. Interested? Apply on the company site.
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About Deepgram

Building foundational AI for speech transcription and understanding.

Deepgram is a foundational AI company on a mission to understand human language. We give any developer access to the most advanced speech AI transcription and understanding with just an API call. Our models deliver the fastest, most accurate transcription alongside contextual features like summarization, sentiment analysis, and topic detection. Beyond that, developers can: 🔊 Process live-streaming or pre-recorded audio 🌎 Transcribe in dozens of languages ⚙️ Train custom models for unique use cases 🔑 Access deep NLU with a unified API 💻 Build in any programming language with our SDKs ✅ Deploy on-prem or on DG’s managed cloud 📈 Get scalable GPU infra for training and inference Deepgram is a proud NVIDIA partner and Y Combinator company, and we recently completed a $72M Series B to define the future of AI Speech Understanding, making us the most-funded speech AI company at its stage.

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