Staff Data Engineer, Audio/ML

What you'll do

• Design and maintain scalable, automated data pipelines for ingesting and preprocessing large-scale audio datasets.
• Develop advanced preprocessing techniques for immersive and multichannel audio formats like Dolby Atmos.
• Automate data cleaning, normalization, and augmentation processes to prepare datasets for various model architectures.
• Create tools and workflows for annotating, labeling, and curating datasets using active learning methods.
• Perform exploratory data analysis to validate dataset quality and identify data gaps.
• 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.
• Support model training and retraining workflows by defining data requirements with AI/ML researchers.

What we're looking for

• Master’s Degree in Data Engineering/Science, Computer Science, Signal Processing, or a related field (preferred PhD).
• 8+ years of experience in data engineering or data science with a focus on building pipelines for AI/ML applications.
• Proficiency in Python with expertise in data manipulation libraries such as Pandas, NumPy, and PyTorch’s data utilities.
• Hands-on experience with audio processing libraries and tools like Librosa, FFmpeg, and SoX for complex audio formats.
• Familiarity with scalable pipeline tools like GitLab, Apache Spark, Airflow, or Luigi and containerized workflows like Docker or Kubernetes.
• Experience with immersive and multichannel audio formats.
• Knowledge of cloud-based platforms and tools for storage and processing such as AWS S3, Redshift, or Google BigQuery.
• Experience integrating data pipelines with AI/ML workflows, including active learning and model retraining (preferred).