Founding Audio AI Research Engineer

About David AI

David AI is the first audio data research company. We bring an R&D approach to data–developing datasets with the same rigor AI labs bring to models. Our mission is to bring AI into the real world, and we believe audio is the gateway. Speech is versatile, accessible, and human—it fits naturally into everyday life. As audio AI advances and new use cases emerge, high-quality training data is the bottleneck. This is where David AI comes in.

David AI was founded in 2024 by a team of former Scale AI engineers and operators. In less than a year, we’ve brought on most FAANG companies and AI labs as customers. We recently raised a $50M Series B from Meritech, NVIDIA, Jack Altman (Alt Capital), Amplify Partners, First Round Capital and other Tier 1 investors.

Our team is sharp, humble, ambitious, and tight-knit. We’re looking for the best research, engineering, product, and operations minds to join us on our mission to push the frontier of audio AI.

About our Research team

As an audio data research company, we believe model capabilities come from high quality, differentiated data. David AI’s research team performs ambitious, long-term research into audio capabilities, while working with internal and external stakeholders to productionalize the latest research insights.

About this role

As a Founding Audio AI Research Engineer , you'll define the research agenda that shapes how the world's best AI labs train their audio models. You’ll have a world‑class workforce of human AI trainers, compute resources, and independence to drive your roadmap.

In this role, you will

• Define and build comprehensive evaluation frameworks for audio AI capabilities across speech, emotion, conversation dynamics, and acoustic patterns.
• Research and prototype novel approaches to audio quality assessment, automated labeling, and data collection optimization.
• Design targeted data collection pipelines for novel, high-value audio capabilities
• Architect automated systems for continuous classifier improvement and prompt engineering evaluation.
• Evaluate frontier models and create actionable research directions.
• Publish findings and papers in top conferences.

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