# Staff Data Engineer (Audio/ML) - Skywalker Sound

**Company**: Skywalker Sound
**Location**: Nicasio, CA
**Work arrangement**: hybrid
**Experience**: staff
**Job type**: full-time
**Salary**: $173,100.00 to $232,100.00 per year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://disney.wd5.myworkdayjobs.com/en-US/disneycareer/job/Nicasio-CA-USA/Staff-Data-Engineer_10129825?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_c9aa854f-162

## Description

The Skywalker Sound Development Group is seeking an experienced Data Engineer with a focus on Audio/ML to specialize in the creation, management, and optimization of data pipelines to support cutting-edge AI/ML research.

As a Data Engineer (Audio/ML), you will focus on developing robust pipelines for processing complex media datasets, enabling AI/ML researchers to build transformative solutions for speech processing, style transfer, and source separation.

**Responsibilities**

- Design, implement, and maintain scalable, automated data pipelines for the ingestion, preprocessing, and transformation of large-scale audio datasets.

- Ensure pipelines support efficient model training and retraining workflows, enabling continuous improvement of AI/ML models.

- Collaborate with AI/ML researchers to define data requirements and integrate feedback to improve data pipeline functionality.

- 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.

- Perform exploratory data analysis to uncover trends, validate dataset quality, and identify 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 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 (e.g., Librosa, FFmpeg, SoX) for handling complex audio formats.

- Familiarity with scalable pipeline tools like GitLab, Apache Spark, Airflow, or Luigi, and experience with containerized workflows (Docker, Kubernetes).

- Strong understanding of data pipeline requirements for model training, retraining, and evaluation in iterative research workflows.

- 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.

- Strong problem-solving skills, with a proactive mindset for addressing evolving data challenges.

**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 how data quality impacts model performance.

- Experience with distributed training pipelines and large-scale dataset processing.

- Contributions to open-source projects or published research in the fields of data science or audio processing.

- Experience with visualization tools (e.g., Tableau, Matplotlib) for quality assurance and exploratory data analysis.

- Expertise in designing systems to support AI/ML model monitoring and retraining over time.

The hiring range for this position in Nicasio, CA is $173,100.00 to $232,100.00 per year.

## Skills

### Required
- Python
- Pandas
- NumPy
- PyTorch
- Librosa
- FFmpeg
- SoX
- GitLab
- Apache Spark
- Airflow
- Luigi
- Docker
- Kubernetes
- AWS S3
- Redshift
- Google BigQuery

### Nice to have
- active learning
- model retraining
- audio-specific datasets
- metadata management
- machine learning principles
- distributed training pipelines
- large-scale dataset processing
- open-source projects
- published research
- visualization tools

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