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Our Data Ops team partners with Research to answer two core questions: What data do we need to train outstanding AI audio models — and how do we source and scale it effectively?
Job Responsibility:
Own the end-to-end lifecycle of data acquisition and labeling operations
Translate research needs into clear data specifications and labeling workflows
Manage external data vendors
Ensure datasets are delivered on time and to high quality standards
Oversee both in-house and third-party labeling teams — setting guidelines, prioritizing work, implementing QC processes, and monitoring throughput and performance
Help scale our operations by improving tooling and processes
Support the hiring, training, and development of a high-performing labeling workforce
Requirements:
Passionate about audio AI driven by a desire to make content universally accessible and breaking the frontiers of new tech
Highly motivated and driven individual with a strong work ethic
Analytical, efficient, and strive on solving complex challenges with a first principles mindset
Consistently strive for excellence, delivering high-quality work quickly and exceeding expectations
Take initiative and work autonomously from day one, prioritizing learning and contribution while leaving ego aside
Strong attention to detail
People skills – comfortable providing feedback and keeping large teams moving along to meet deadlines
Nice to have:
Prior experience running large-scale data labelling projects for a FAANG/similar company, AI research lab, or data labelling platform