Music Data Engineer

Overview

Summary 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.

Summary 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.

Description 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.

Responsibilities

• Analyze and evaluate music datasets for musical characteristics and high quality across genres.
• Analyze musical characteristics of large-scale datasets using Python.
• Create and curate music datasets through recording sessions, synthetic data generation, and musical content creation.
• Collaborate with data engineers to develop data curation strategies that ensure musical authenticity and diversity.
• Validate model outputs and training results for musical correctness, coherence, and creative quality.
• Document musical characteristics, metadata requirements, and quality standards for music datasets.
• Design evaluation criteria and test cases that assess ML models from a musical perspective.
• Work with ML researchers and engineers to communicate musical requirements and provide domain expertise.

Minimum Qualifications

• Bachelor’s degree (or higher) in Audio Production, Musicology, Music Technology, or related field, or 3 years of equivalent professional work experience
• Experience with Python programming and data analysis using pandas or similar libraries
• Understanding of machine learning concepts and audio signal processing fundamentals
• Advanced knowledge of music theory, harmony, composition, and instrumentation across multiple genres

Preferred Qualifications

• Familiarity with music analysis libraries such as librosa or music21
• Experience analyzing large music datasets or music information retrieval research
• Experience curating large-scale, multi-language music datasets for LLM fine-tuning and model adaptation workflows
• Expert in music creation tools such as Logic Pro
• Experience working on music research or data-driven music projects
• Experience with data visualization for music analysis or dataset exploration
• Knowledge of ML model evaluation methodologies and quality metrics
• Strong attention to detail and commitment to data accuracy and quality
• Fluent in English

At Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced, and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. We will work with applicants to make any reasonable accommodations.