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Deepgram
Deepgram
51 – 200 Darbuotojai
H1B vizos rėmėjasKonsultavimasLogistikaSveikatos priežiūra
„Deepgram“ yra balso dirbtinio intelekto įmonė, kurianti API kalbai į tekstą, tekstui į kalbą ir kalbai suprasti. Jos platforma padeda kūrėjams kurti realiojo laiko balso programas, įskaitant pokalbinį dirbtinį intelektą, kontaktų centrų įrankius ir medicininės transkripcijos darbo eigas. Tiksliai, greitai ir ekonomiškai apdorodama sakytinę kalbą, „Deepgram“ padeda sveikatos priežiūros, logistikos, konsultavimo ir kitų sričių organizacijoms paversti balso duomenis naudinga informacija bei interaktyvia patirtimi.
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Darbai įmonėje

Staff Researcher, Voice AI Foundations

Develop latent-space models for Deepgram’s voice AI platform, advancing neural audio codecs, generative speech, multimodal systems, and efficient inference on hardware.

AUAustralijaNuotolinisVisas etatasVadovasPrivaloma Kalba: anglųDirbtinis intelektas

Aprašymas

  • Develop neural audio codecs that combine very low bit rates with high-fidelity reconstruction.
  • Create steerable generative models for varied human speech, including emotional, multi-speaker, noisy, and overlapping speech.
  • Build embedding systems that separate latent representations into speaker, content, style, environment, and channel dimensions.
  • Recombine latent representations to generate synthetic audio data at unprecedented scale.
  • Train multimodal speech-to-speech systems for robust understanding and empathetic, human-like responses.
  • Design model architectures, training methods, and inference algorithms for bare-metal hardware.
  • Support cost-effective training on datasets spanning billions of hours and real-time inference for hundreds of millions of concurrent conversations.
  • Run controlled experiments, ablations, evaluations, stress tests, and edge-case analyses.
  • Advance foundational research in voice AI, speech, and language modeling.

Reikalavimai

  • Strong grounding in mathematics and statistical learning theory, especially self-supervised and multimodal learning.
  • Deep knowledge of foundation model architectures and scaling training across multiple modalities.
  • Ability to derive new mathematical formulations and implement them efficiently.
  • Demonstrated ability to build and curate large datasets while maintaining quality and diversity.
  • Proven experience designing controlled experiments to test architectural innovations and 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.
  • Comfort using and experimenting with advanced AI tools.
  • Ability to identify decisive experiments that quickly validate or disprove ideas.
  • Vision for scaling successful proofs of concept by a factor of 100.
  • Experience or knowledge spanning neural audio codecs, generative models, embedding systems, latent representations, multimodal speech-to-speech systems, model architectures, training methods, inference algorithms, and hardware-efficient deployment.

Privalumai

  • Remote work arrangement.
  • AI-first environment that encourages using and experimenting with advanced AI tools.
  • Opportunity to pursue transformative voice AI research at scale.
  • Opportunities to contribute to open source and publish research.
  • The interview AI Notetaker recording may be declined without affecting candidacy.

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