Audio datasets for speech and conversational AI
David AI builds high-quality proprietary audio datasets for speech recognition, translation, synthesis, and conversational AI models. They serve top AI labs and enterprises like Mag7 companies, enabling natural voice interactions in AI applications such as robots and assistants. As the first audio data research lab, they address challenges in data diversity, quality, and scale for audio AI training.
Own end-to-end contributor growth for David AI's audio data network, including acquisition strategy, high-velocity experiments, funnel optimization, and product/incentive iteration to scale voice actors, linguists and transcribers.
Lead full-stack development of scalable audio data platforms, building features and mentoring engineers in a fast-paced AI startup. Requires 6+ years of product-focused full-stack experience and technical leadership.
Build full-stack tools and scalable data pipelines for audio AI research. Work closely with researchers to iterate on data collection and processing interfaces.
Build and maintain scalable backend services and distributed systems for audio data pipelines at a Series B audio AI startup. Requires 3+ years backend experience with strong distributed systems and cloud infrastructure skills.
Build cutting-edge speech and audio ML models, production inference systems, and resilient pipelines. Own full ML lifecycle from research to deployment on terabytes of audio data.
Leads end-to-end customer journeys for audio AI initiatives, builds consultative relationships with stakeholders, translates objectives into actionable strategies with research/engineering teams, and uncovers growth opportunities. Requires 4+ years experience, AI data familiarity, and strong project management.
Leads full-stack development of audio data products, building scalable systems for processing terabytes of data, deploying LLM/DSP solutions, and mentoring engineers. Requires 6+ years full-stack experience with strong web fundamentals and technical leadership.
Builds and maintains scalable backend services and distributed systems to process petabytes of audio data. Requires 3+ years backend experience with APIs, databases, cloud infra, and distributed systems.
Leads Data Operations team to build and scale audio data pipelines from experiments to production for AI models. Partners with researchers and executives, drives metrics-based decisions, and ensures high-quality data delivery at scale. Requires 5-10 years high-ownership experience and SQL proficiency.
Leads design, scaling, and ownership of data pipelines transforming raw audio into high-quality datasets for AI labs. Collaborates with researchers, engineers, and product teams using SQL and systems thinking in high-intensity ops environments (2-6 YOE required).
Builds full-stack features, scalable data processing pipelines, and LLM/DSP solutions for audio data tools used by AI researchers. Requires 2+ years full-stack experience in high-paced environments, with excitement for AI/ML.