Research Staff, Voice AI Foundations
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Job Description
Research Staff pioneering latent-space models for codecs, speech generation, and multimodal voice AI. Advancing Deepgram’s real-time speech platform through scalable, efficient foundational research.
Responsibilities:
- Develop next-generation neural audio codecs with extreme low-bit-rate compression and high-fidelity reconstruction across world-scale general audio corpora
- Pioneer steerable generative models synthesizing diverse human speech, including emotional expression, multi-speaker scenarios, environmental noise, and overlapping speech
- Develop embedding systems that factorize codec latent spaces into interpretable speaker, content, style, environment, and channel dimensions
- Use latent recombination to generate synthetic audio data at unprecedented scales
- Train multimodal speech-to-speech systems that understand diverse humans and produce empathic, human-like responses
- Design model architectures, training schemes, and inference algorithms adapted to bare-metal hardware for cost-efficient billion-hour dataset training and real-time inference
- Conduct foundational research in Latent Space Models addressing data, scale, and cost challenges in voice AI
- Design controlled experiments, ablations, evaluations, stress tests, and benchmarks to validate research hypotheses
- Collaborate through open-source contributions and research publications advancing speech and language AI
Requirements:
- Strong mathematical foundation in statistical learning theory, particularly areas relevant to self-supervised and multimodal learning
- Deep expertise in foundation model architectures and scaling training across multiple modalities
- Proven ability to bridge theory and practice by deriving novel mathematical formulations and implementing them efficiently
- Demonstrated ability to build data pipelines that process and curate massive datasets while maintaining quality and diversity
- Track record of designing controlled experiments that isolate architectural innovations and validate theoretical insights
- Experience optimizing models for real-world deployment, including hardware constraints and efficiency techniques
- History of open-source contributions or research publications advancing speech/language AI
- Ability to identify critical experiments that validate or disprove ideas quickly
- Vision to scale successful proofs-of-concept 100x
- Comfort using AI to automate and amplify personal impact
- Ability to adapt quickly, experiment, learn constantly, and work in a rapidly changing AI environment
Benefits:
- Remote work arrangement
- AI-first work environment with active use and experimentation of advanced AI tools
- Opportunity to pioneer foundational voice AI research with transformative impact
- Opportunity to contribute to open-source projects and research publications
- AI Notetaker interview recording is optional; opting out does not impact candidacy
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