About the job
Join David AI as a Product Manager
David AI is pioneering the audio data research landscape, harnessing an R&D methodology to develop high-quality datasets that support AI models. Our vision is to seamlessly integrate AI into everyday experiences, with audio serving as the perfect entry point. As the demand for advanced audio AI solutions grows, the necessity for superior training data becomes paramount, and David AI is at the forefront of this evolution.
Founded in 2024 by a talented team of former engineers from Scale AI, we’ve quickly established ourselves as a trusted partner for leading FAANG companies and AI labs. Our recent $50M Series B funding round, led by top-tier investors including Meritech and NVIDIA, is a testament to our trajectory and potential.
We pride ourselves on our team’s intelligence, humility, and ambition. If you are passionate about research, engineering, product development, or operations, we invite you to contribute to our mission of advancing audio AI.
The Product Team
Our Product team operates at the intersection of research, engineering, and operations, crafting audio data products that empower cutting-edge machine learning models. We translate groundbreaking research into scalable and thoughtful data products that prioritize quality, coverage, and clarity for model creators.
Your Role
As a Product Manager on our Product team, you will take charge of the strategy and execution of key components within our audio data portfolio. Collaborating closely with the Research, ML, Engineering, and Operations teams, you will convert model requirements into actionable data roadmaps, quality standards, and deliverables ready for clients.
Key Responsibilities
- Specialize in areas such as data roadmap expansion, quality metrics, and evaluation based on your interests and background.
- Lead product strategy and development for audio data offerings, guiding them from research to full-scale production.
- Convert research insights into specific requirements for data collection, quality assurance, and documentation.
- Work in partnership with Research, ML, Engineering, and Operations teams to operationalize new datasets and capabilities.
- Establish metrics and success criteria to prioritize initiatives and evaluate impact across quality, coverage, cost, and speed.

