Research Staff, Voice AI Foundations

Deepgram

. Develop next-generation neural audio codecs with extreme low-bit-rate compression and high-fidelity reconstruction across world-scale general audio corpora .

Posted 10/11/2026full-timeRemote • United KingdomLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing neural audio codecs and multimodal speech systems, with a strong foundation in statistical learning theory and experience in optimizing models for real-world deployment. Proven ability to design controlled experiments and contribute to open-source research in speech and language AI.

Highest-signal resume keywords

Neural Audio Codec DevelopmentStatistical Learning TheoryMultimodal LearningData Pipeline DesignOpen-Source Contributions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Neural Audio CodecsGenerative ModelsLatent Space ModelsModel Architecture DesignStatistical Learning TheoryData Pipeline DevelopmentReal-Time Inference OptimizationControlled Experiment DesignMultimodal Speech SystemsMathematical Formulation Implementation

Soft Skills

AdaptabilityCollaborationCritical ThinkingEmpathyContinuous Learning

Industry Keywords

Speech AILanguage AISelf-Supervised LearningBillion-Hour Dataset TrainingAI Automation

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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