Research Staff, Voice AI Foundations [UK - Remote]
Salary not listed • Remote • London, UK • Full-time
Posted 20d ago
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Research Engineer
About the job
Deepgram is a leading platform for Voice AI, providing real-time APIs for speech-to-text, text-to-speech, and building voice agents. We are seeking a Research Staff member to pioneer the development of Latent Space Models (LSMs), a novel approach to address fundamental data, scale, and cost challenges in building robust, contextualized voice AI. This role requires an AI-first mindset, comfort with rapid experimentation, and a drive to push the boundaries of AI technologies. You will work on challenges that require entirely new paradigms for audio AI to make voice interaction accessible to everyone.
Responsibilities
- Develop next-generation neural audio codecs for extreme, low bit-rate compression and high fidelity reconstruction.
- Pioneer steerable generative models for synthesizing diverse human speech from latent representations.
- Develop embedding systems to factorize codec latent space into interpretable dimensions for precise control.
- Leverage latent recombination to generate synthetic audio data at scale for joint model and data scaling.
- Train multimodal speech-to-speech systems capable of understanding diverse human inputs and producing empathic responses.
- Design model architectures, training schemes, and inference algorithms optimized for cost-efficient training and real-time inference.
Requirements
- Strong mathematical foundation in statistical learning theory, especially self-supervised and multimodal learning.
- Deep expertise in foundation model architectures and scaling training across modalities.
- Proven ability to bridge theory and practice, deriving novel mathematical formulations and implementing them efficiently.
- Demonstrated ability to build data pipelines for processing and curating massive datasets.
- Track record of designing controlled experiments to validate theoretical insights and architectural innovations.
- Experience optimizing models for real-world deployment, considering hardware constraints and efficiency.