About the job
Join David AI
At David AI, we are pioneering the audio data research landscape. Our research and development approach to data ensures that we deliver datasets with the same precision and rigor that leading AI labs apply to their models. Our mission is to seamlessly integrate AI into everyday life, leveraging audio as a key channel. As we witness advancements in audio AI and the emergence of new use cases, we recognize that high-quality training data is the critical component. This is where David AI steps in.
Founded in 2024 by a group of former engineers and operators from Scale AI, we have rapidly established partnerships with major FAANG companies and AI labs. Recently, we secured a $50M Series B funding round from prominent investors including Meritech, NVIDIA, Jack Altman (Alt Capital), Amplify Partners, and First Round Capital.
Our team is sharp, humble, and ambitious. We are on the lookout for talented individuals in research, engineering, product management, and operations to join us in our mission to redefine the audio AI landscape.
About Our Machine Learning Team
Our Machine Learning team operates at the forefront of innovative research and practical application, transforming raw audio into high-quality data for top AI labs and enterprises. We manage the entire machine learning lifecycle—from exploring novel speech processing algorithms to deploying models that handle terabytes of audio data daily.
Your Role
As an Applied ML Engineer at David AI, you will develop state-of-the-art speech and audio models, establish production inference systems, and create robust pipelines that demonstrate the true potential of high-quality data.
Key Responsibilities
Research and Design: Create solutions using advanced signal processing algorithms and cutting-edge ML models tailored for speech and audio applications.
Development: Build production-grade inference algorithms, pipelines, and APIs in collaboration with cross-functional teams to extract valuable insights for our clients.
Collaboration: Work alongside our Operations team to gather valuable training and evaluation datasets to enhance our model quality.
Architecture: Design systems that ensure durable and resilient inference and evaluations.

