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A leading technology company in Berlin is looking for a Music Data Specialist to analyze music datasets for machine learning applications. This role requires a deep understanding of music theory, Python programming skills, and experience with audio data analysis. The ideal candidate will ensure that datasets meet high standards of musical authenticity and diversity for AI-powered music tools.
Berlin, Berlin, Germany Software and Services
Imagine what you could do here. At Apple, new ideas have a way of becoming great products very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.The Music Creation Apps team is seeking a music data specialist to bring deep musical expertise to our ML development workflows. Working closely with content, data and machine learning engineers, you'll evaluate music datasets from a musicological perspective, ensuring data quality through your understanding of music theory, composition, and instrumentation.You'll use Python and specialized music libraries to analyze musical characteristics, identify gaps in dataset representation, and validate the musical accuracy of our training data. This unique role combines your passion for music with technical skills to maintain Apple's high standards for musical authenticity and creative excellence in our AI-powered music creation tools.
As a Music Data Specialist for Music Creation Apps, you'll apply your music theory and production knowledge to evaluate and curate datasets to train machine learning models. You'll provide the musical expertise needed to assess dataset quality, identify musical biases or gaps, and validate that our training data represents diverse musical styles and genres authentically.Using Python and music analysis libraries like librosa, you'll analyze and evaluate musical characteristics to ensure our datasets meet the high standards required for professional music creation applications. You'll also contribute to building evaluation frameworks that assess model outputs from a musical perspective, helping bridge the gap between technical ML metrics and real-world musical quality.