Deepgram
Deepgram
Deepgram este o companie de inteligență artificială vocală care dezvoltă API-uri pentru conversia vorbirii în text, a textului în vorbire și pentru înțelegerea limbajului. Platforma sa îi ajută pe dezvoltatori să creeze aplicații vocale în timp real, inclusiv soluții de inteligență artificială conversațională, instrumente pentru centre de contact și fluxuri de lucru pentru transcriere medicală. Prin procesarea limbajului vorbit, cu accent pe acuratețe, viteză și eficiență din punct de vedere al costurilor, Deepgram permite organizațiilor din domeniul sănătății, logisticii, consultanței și din alte sectoare să transforme datele vocale în informații utile și experiențe interactive.

Lead Research Scientist, Voice AI Foundations

Advance foundational voice AI research at Deepgram, from latent-space models and neural audio codecs to speech-to-speech systems. Build scalable, efficient technology for real-time human–machine interaction.

Descriere

  • Develop latent-space models to tackle core data, scale, and cost challenges in robust, contextual voice AI.
  • Create neural audio codecs that combine very low bit rates with high-fidelity reconstruction.
  • Build steerable generative models for varied speech, including emotional, multi-speaker, noisy, and overlapping speech.
  • Design embedding systems that separate latent representations into speaker, content, style, environment, and channel dimensions.
  • Apply latent recombination to produce synthetic audio data at unprecedented scale.
  • Train multimodal speech-to-speech systems for broad understanding and empathetic, human-like responses.
  • Design model architectures, training methods, and inference algorithms for bare-metal hardware.
  • Support cost-efficient training on billion-hour datasets and real-time inference across hundreds of millions of concurrent conversations.
  • Run controlled experiments, ablation studies, evaluations, and stress tests.
  • Contribute to research publications and open-source projects.

Cerințe

  • Strong mathematical grounding in statistical learning theory, especially self-supervised and multimodal learning.
  • Deep knowledge of foundation model architectures and scaling training across multiple modalities.
  • Ability to develop original mathematical formulations and implement them efficiently.
  • Proven ability to build and maintain large, high-quality, diverse data pipelines.
  • Experience designing controlled experiments to isolate architectural effects and test theoretical insights.
  • Experience preparing models for real-world deployment, including hardware constraints and efficiency methods.
  • Record of open-source contributions or research publications advancing speech or language AI.
  • Ability to identify decisive experiments that quickly test ideas.
  • Vision for scaling successful proofs of concept by 100 times.
  • An AI automation and augmentation mindset.
  • Scholarly literature or academic publications related to speech processing, speech-to-text, text-to-speech, or similar work, as requested in the application.
  • Legal authorization to work in the country where the role is based.
  • Ability to meet visa sponsorship requirements for the country where the role is based.

Beneficii

  • Remote work arrangement.
  • AI-first work environment with opportunities to use and experiment with advanced AI tools.
  • Opportunity to work on transformative voice AI research.
  • Opportunity to contribute to open-source projects and research publications.
  • Interview recording with the AI Notetaker is optional; opting out does not affect candidacy.

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