Staff Data Engineer, Audio Content & Dataset Science (Skywalker Sound)

Job Requirements The Skywalker Sound Development Group is seeking an experienced Staff Data Engineer to lead the end-to-end strategy, creation, management, and optimization of audio content datasets to drive state-of-the-art AI/ML research. In this role, you will take principal ownership of building, curating, and balancing high-quality audio datasets tailored for the training, retraining, and evaluation of machine learning models in immersive and multichannel audio applications. As a Staff Data Engineer, you will focus on developing scalable pipelines to process complex media content, while actively analyzing and balancing datasets relative to model performance for speech processing, style transfer, and source separation. Your expertise in audio content and dataset lifecycle management will directly empower AI/ML researchers to build transformative soundtrack workflows for global media production. This role is considered Hybrid, which means the employee will be based in the Bay Area and work onsite in our Skywalker Sound office in Nicasio and occasionally from home. Responsibilities Take principal ownership of creating, curating, and managing high-volume audio content datasets to fuel AI/ML model training, retraining, and evaluation. Design scalable data pipelines for the ingestion, preprocessing, and continuous transformation of large-scale, complex audio content. Analyze, balance, and fine-tune dataset compositions relative to model performance metrics, mitigating bias and ensuring optimal generalization across AI/ML training iterations. Develop advanced preprocessing techniques for immersive and multichannel audio formats (e.g., Dolby Atmos, high-order ambisonics). Automate data cleaning, normalization, and augmentation processes to prepare datasets for various model architectures, including foundational models and transformers. Integrate external datasets and APIs while ensuring compliance with legal and ethical data usage standards. Monitor and optimize pipeline performance to handle complex and dynamic data structures effectively. Create tools and workflows for annotating, labeling, and curating datasets, including the use of active learning methods…