About the job
Join David AI as a Staff Product Engineer
At David AI, we are pioneering the field of audio data research, applying a rigorous R&D methodology to create robust datasets that empower AI models. Our vision is to integrate AI seamlessly into daily life, with audio serving as the key medium. As the demand for high-quality training data grows, we position ourselves as the solution.
Founded in 2024 by a team of seasoned engineers from Scale AI, we quickly garnered the trust of leading FAANG companies and AI labs. Our recent $50M Series B funding round was led by prestigious investors including Meritech and NVIDIA, underscoring our potential in the audio AI landscape.
We pride ourselves on our dynamic, collaborative, and ambitious team, and we are on the lookout for exceptional talent in research, engineering, product, and operations to help us redefine audio AI.
About Our Engineering Team
Our engineering team is at the forefront of innovation, building the infrastructure, platforms, and models that convert raw audio into valuable data for top-tier AI labs and enterprises. We are a close-knit group of product engineers, infrastructure experts, and machine learning specialists who are dedicated to establishing the first-ever audio data research organization.
We operate with agility, taking ownership of our projects from inception to deployment, delivering production-ready solutions on a daily basis. Our engineers design real-time pipelines that manage vast amounts of speech data while deploying advanced generative audio models.
Your Role
As a Staff Product Engineer, you will spearhead our Product Engineering team, developing state-of-the-art products that enable our clients to harness audio data effectively for model training. You will collaborate with researchers to continually refine our data collection methodologies.
Your Responsibilities
Lead the development of full-stack features, rapidly iterating to deliver daily innovations to our users.
Construct scalable systems that process terabytes of audio data and extract actionable insights.
Deploy and assess LLM- and DSP-based solutions to enhance customer understanding of their data.
Facilitate research iterations and deployment interfaces for data collection in collaboration with researchers and operations.

