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.

What you'll do:

• 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.

• Perform exploratory data analysis and continuous dataset audit logs to uncover distribution imbalances, validate content quality, and resolve critical data coverage gaps.

What you'll need:

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

• 8+ years of data engineering or data science experience, with a heavy emphasis on handling complex audio/media content and owning ML training datasets.

• 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).

• Proven track record of managing dataset lifecycles and balancing dataset distributions to directly improve model accuracy, loss, and generalization in iterative research environments.

• 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:

• Deep expertise in audio domain content, including specialized knowledge of dataset balancing techniques for complex, multi-speaker, or spatial audio environments.

• 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.

Disclaimer

PAY TRANSPARENCY

The hiring range for this position in San Francisco is $173,100 to $232,100. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

This position is with Lucasfilm Ent Co Ltd, LLC Payroll Svc, which is part of a business we call Skywalker Sound.

To learn more about Disney's guidelines on the use of AI for candidates, click here. (https://join.disneycareers.com/global/en/ai-usage)

Lucasfilm Ent Co Ltd, LLC Payroll Svc is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Disney champions a business environment where ideas and decisions from all people help us grow, innovate, create the best stories and be relevant in a constantly evolving world.

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The Walt Disney Company and its Affiliated Companies are Equal Employment Opportunity employers and welcome all job seekers including individuals with disabilities and veterans with disabilities. If you have a disability and believe you need a reasonable accommodation in order to search for a job opening or apply for a position, email [email protected] with your request. This email address is not for general employment inquiries or correspondence. We will only respond to those requests that are related to the accessibility of the online application system due to a disability.