The Staff Data Engineer (Audio/ML) role supports immersive and multichannel audio AI/ML research by designing and operating scalable data pipelines. You will focus on reliable ingestion, preprocessing, dataset curation, and automated workflows that enable training, retraining, and evaluation of audio models.
Responsibilities
- Design, implement, and maintain scalable automated data pipelines for ingestion, preprocessing, and transformation of large-scale audio datasets.
- Build pipelines that support efficient training and retraining workflows for continuous improvement of AI/ML models.
- Partner with AI/ML researchers to define data requirements and incorporate feedback to improve pipeline functionality.
- Develop preprocessing methods for immersive and multichannel audio formats, including Dolby Atmos and high-order ambisonics.
- Automate data cleaning, normalization, and augmentation for multiple model architectures, including foundational models and transformers.
- Integrate external datasets and APIs while maintaining compliance with legal and ethical data usage standards.
- Monitor and optimize pipeline performance to handle complex and evolving data structures.
- Create tools and workflows for dataset annotation, labeling, and curation, including support for active learning approaches.
- Conduct exploratory data analysis to identify trends, validate dataset quality, and detect data gaps.
Requirements
- Master’s Degree with preference for 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 libraries such as Pandas, NumPy, and PyTorch data utilities.
- Hands-on experience with audio processing tools and libraries, including Librosa, FFmpeg, and SoX for complex audio formats.
- Experience with scalable pipeline technologies such as GitLab, Apache Spark, Airflow, or Luigi, plus experience with containerized workflows using Docker and Kubernetes.
- Strong understanding of data pipeline requirements for model training, retraining, and evaluation within iterative research workflows.
- Experience working with immersive and multichannel audio formats.
- Knowledge of cloud storage and processing tools such as AWS S3, Redshift, or Google BigQuery.
- Strong problem-solving skills and a proactive mindset for addressing 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 datasets and metadata management strategies.
- Knowledge of machine learning principles and the relationship between data quality and model performance.
- Experience with distributed training pipelines and large-scale dataset processing.
- Contributions to open-source projects or published research in data science or audio processing.
- Experience using visualization tools such as Tableau and Matplotlib for data quality assurance and exploratory analysis.
- Expertise designing systems that support AI/ML model monitoring and retraining over time.
Location and Compensation
- Location: Nicasio, CA (hybrid)
- Salary: USD 173,100 - 232,100 per year
