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Research Staff, Voice AI Foundations - Remote

Deepgram· Research
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RemoteFull-timePosted 1w ago

About the role

Research Staff role focused on developing Latent Space Models (LSMs) for scalable, high-fidelity voice AI, including neural audio codecs, steerable generative models, latent space factorization, and hardware-efficient training/inference for real-time, large-scale applications. Remote position based in The Netherlands.

Responsibilities

  • Pioneer development of Latent Space Models (LSMs) for voice AI enablement across data, scale, and cost challenges.
  • Build next-generation neural audio codecs with extreme low bit-rate compression and high fidelity reconstruction across large audio corpora.
  • Pioneer steerable generative models to synthesize diverse human speech from codec latent representations across varied speaking styles and environments.
  • Develop embedding systems to factorize codec latent space into interpretable dimensions (speaker, content, style, environment, channel effects) and enable latent recombination for data amplification.
  • Leverage latent recombination to generate synthetic audio data at scale and explore joint model-data scaling for audio; train multimodal speech-to-speech systems.
  • 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 relevant to self-supervised and multimodal learning.
  • Deep expertise in foundation model architectures with understanding of scaling training across modalities.
  • Proven ability to bridge theory and practice with implementable formulations.
  • Experience building data pipelines for massive datasets with quality and diversity.
  • Track record designing controlled experiments to isolate architectural impacts and validate insights.
  • Experience optimizing models for real-world deployment, including hardware constraints and efficiency techniques.
  • History of open-source contributions or publications advancing speech/language AI.

Skills

Required:Statistical learning theoryFoundation model architecturesData pipelines for large datasetsExperimental designHardware-aware model optimization
Nice to have:Open-source/publications

Benefits

  • Not specified in description
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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