Staff Data Engineer (Audio/ML)

The Staff Data Engineer (Audio/ML) role focuses on building and operating scalable data pipelines that enable immersive and multichannel audio research for AI/ML initiatives. The position emphasizes automated dataset preparation, rigorous quality practices, and data systems that support iterative training and evaluation.

Key Responsibilities

• Design, implement, and maintain scalable automated pipelines for ingestion, preprocessing, and transformation of large-scale audio datasets.
• Build pipeline capabilities that support efficient model training and retraining workflows to support continuous AI/ML improvement.
• Partner with AI/ML researchers to define data needs, incorporate feedback, and refine pipeline functionality.
• Create advanced preprocessing methods for immersive and multichannel audio formats, including Dolby Atmos and high-order ambisonics.
• Automate data cleaning, normalization, and augmentation workflows for datasets intended for foundational models and transformer-based architectures.
• Integrate external datasets and APIs while ensuring compliance with legal and ethical standards for data usage.
• Monitor and optimize pipeline performance to manage complex and evolving data structures effectively.
• Develop tools and workflows for dataset annotation, labeling, and curation, including active learning approaches.
• Conduct exploratory data analysis to identify trends, validate dataset quality, and locate gaps in data coverage.

Required Qualifications

• Master’s Degree, with preference for a PhD in Data Engineering/Science, Computer Science, Signal Processing, or a related field.
• 8+ years of experience in data engineering or data science, focused on building pipelines for AI/ML applications.
• Proficiency in Python, including data manipulation experience with Pandas, NumPy, and PyTorch’s data utilities.
• Hands-on experience with audio processing libraries and tools such as Librosa, FFmpeg, and SoX.
• Familiarity with scalable pipeline tools including GitLab, Apache Spark, Airflow, or Luigi, plus experience with containerized workflows using Docker and Kubernetes.
• Strong understanding of pipeline requirements for iterative research cycles involving model training, retraining, and evaluation.
• Experience with immersive and multichannel audio formats.
• Knowledge of cloud-based platforms for storage and processing, including AWS S3, Redshift, or Google BigQuery.
• Strong problem-solving skills and a proactive approach to handling evolving data challenges.

Technologies

• Python, Pandas, NumPy, PyTorch
• Librosa, FFmpeg, SoX
• GitLab, Apache Spark, Airflow, Luigi
• Docker, Kubernetes
• AWS S3, Redshift, Google BigQuery
• Dolby Atmos, high-order ambisonics
• Tableau, Matplotlib

Preferred Qualifications

• Experience integrating data pipelines with AI/ML workflows, including active learning and model retraining.
• Familiarity with audio-specific dataset practices and metadata management strategies.
• Knowledge of machine learning principles and how data quality affects model performance.
• Experience with distributed training pipelines and large-scale dataset processing.
• Contributions to open-source work or published research in data science or audio processing.
• Experience using visualization tools such as Tableau and Matplotlib for quality assurance and exploratory analysis.
• Expertise designing systems that support ongoing AI/ML model monitoring and retraining over time.

Location and Compensation

• Location: Nicasio, CA (hybrid)
• Compensation: USD 173,100 - 232,100 per year

Education

Master’s Degree is required, with preference for a PhD in Data Engineering/Science, Computer Science, Signal Processing, or a related discipline.