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? In this role, you will own the end-to-end lifecycle of data acquisition and labeling operations. You'll translate research needs into clear data specifications and labeling workflows, manage external data vendors, and ensure datasets are delivered on time and to high quality standards. You'll oversee both in-house and third-party labeling teams — setting guidelines, prioritizing work, implementing QC processes, and monitoring throughput and performance. You'll also help scale our operations by improving tooling and processes, and by supporting the hiring, training, and development of a high-performing labeling workforce.
We're looking for exceptional individuals who combine technical excellence with ethical awareness, who are excited by hard problems and motivated by human impact. You'll strive with us if you:
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Are passionate about audio AI driven by a desire to make content universally accessible and breaking the frontiers of new tech.
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Are a highly motivated and driven individual with a strong work ethic. Our team is aware of this critical moment of audio AI evolution and is committed to going the extra mile to lead.
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Are analytical, efficient, and strive on solving complex challenges with a first principles mindset.
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Consistently strive for excellence, delivering high-quality work quickly and exceeding expectations.
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Take initiative and work autonomously from day one, prioritizing learning and contribution while leaving ego aside.
We don't require any formal experience, certifications, or degrees. Instead, we're looking for someone with:
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Strong attention to detail – data is all about the details; we expect you to craft clear project guidelines together with Research and build processes to ensure our data meets them
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People skills – you will interact with Researchers, external service providers, and a large number of data labellers and labelling managers; you should feel comfortable providing feedback and keeping large teams moving along to meet deadlines
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Bonus points for:
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Prior experience running large-scale data labelling projects for a FAANG/similar company, AI research lab, or data labelling platform
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Python skills
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