Sr Staff R&D Engineer

Reposted 21 Days Ago
Be an Early Applicant
Nicasio, CA, USA
In-Office
206K-277K Annually
Senior level
Digital Media • Gaming • News + Entertainment • Sports
The Role
Lead the development of machine learning technologies for audio processing, focusing on speech and generative audio models. Collaborate across engineering, manage model lifecycles, and mentor junior staff.
Summary Generated by Built In

Job Posting Title:

Sr Staff R&D Engineer

Req ID:

10127968

Job Description:

The Skywalker Sound Development Group is seeking a highly accomplished Sr Staff R&D Engineer (AI/ML) to lead the development of transformative audio intelligence technologies for global media production. This senior-level role is central to advancing our next-generation soundtrack platform, with a focus on speech processing, style transfer, upmixing, source separation, and generative audio synthesis.

You will architect, build, and optimize cutting-edge machine learning systems at scale—leveraging foundational models, neural vocoders, latent diffusion models, and advanced retraining workflows. As a core member of our applied R&D team, you will contribute to technical direction, collaborate across product and engineering, and deliver production-ready solutions that integrate seamlessly into creative and operational workflows for elite content creators worldwide.

This role is considered Hybrid, which means the employee will work onsite in our Nicasio, CA office and occasionally from home.

What You’ll Do

  • Lead the research, design, and implementation of state-of-the-art machine learning algorithms for speech processing, voice transfer, source separation, and upmixing in media post-production environments.

  • Drive the architecture and deployment of scalable model training pipelines using PyTorch and distributed computing frameworks.

  • Develop novel generative audio models, including latent diffusion, flow-based models, variational autoencoders, and neural vocoders, optimized for professional soundtrack production.

  • Own end-to-end model lifecycle management: pretraining, fine-tuning, validation, inference optimization, and CI/CD integration.

  • Guide the development of personalized model adaptation workflows to support per-user tuning, cross-project continuity, and flexible deployment.

  • Collaborate with product, platform, and engineering leads to define integration strategies within a secure, cloud-optimized SaaS environment.

  • Stay at the forefront of generative audio, multi-modal modeling, and self-supervised learning—translating emerging research into applied innovation.

  • Contribute to internal tooling and infrastructure that improves iteration speed, reproducibility, and explainability of deployed models.

  • Mentor junior researchers and engineers, and contribute to a culture of rigorous experimentation, collaboration, and continuous improvement.

What We’re Looking For

  • MSc or PhD in Computer Science, Electrical Engineering, Applied Math, or a related field with a focus on AI/ML and mult-imodal signal processing.

  • 5 years of professional experience in applied ML, with a deep focus on audio-centric AI/ML research and deployment.

  • Expertise in building and scaling models using PyTorch, with fluency in training, fine-tuning, and inference for deep neural networks.

  • Demonstrated experience developing generative models such as VAE, GAN, diffusion models, or neural vocoders (e.g., HiFi-GAN, WaveNet).

  • Deep understanding of audio-specific ML domains, including source separation, speech enhancement, music processing, and cross-modal tasks.

  • Experience with MLOps tooling (e.g., Weights & Biases, MLflow, Datachain), Docker-based containerization, and scalable infrastructure for distributed training.

  • Fluency in audio signal processing fundamentals and the integration of DSP into ML pipelines.

  • Proven ability to contribute to architectural planning, research strategy, and production deployment in complex, multi-stakeholder environments.


Preferred Qualifications

  • Familiarity with audio/text/video multi-modal frameworks and cross-domain representations.

  • Experience implementing real-time or near-real-time inference pipelines in cloud or edge environments (e.g., AWS, GCP, on-prem GPUs).

  • Working knowledge of latent diffusion audio models (e.g., stable-audio, AudioLDM, AudioGen).

  • Strong knowledge of industry-standard audio datasets and benchmarks (LibriSpeech, VCTK, MUSDB, etc.).

  • Experience optimizing inference pipelines for creative applications or interactive use.

  • Proficiency in lower-level audio frameworks (C / C++, etc.)

  • Contributions to published research at top-tier conferences (NeurIPS, ICASSP, ICLR, Interspeech) and/or open-source ML frameworks.

The hiring range for this position in Nicasio, CA is $206,400 to $276,700 per year. 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.

Job Posting Segment:

Skywalker Sound

Job Posting Primary Business:

Skywalker Sound-Engineering

Primary Job Posting Category:

Software Engineer

Employment Type:

Full time

Primary City, State, Region, Postal Code:

Nicasio, CA, USA

Alternate City, State, Region, Postal Code:

Date Posted:

2025-08-19

Skills Required

  • MSc or PhD in Computer Science, Electrical Engineering, Applied Math, or a related field
  • 5 years of professional experience in applied ML
  • Expertise in building and scaling models using PyTorch
  • Experience developing generative models such as VAE, GAN, diffusion models, or neural vocoders
  • Deep understanding of audio-specific ML domains
  • Experience with MLOps tooling
  • Fluency in audio signal processing fundamentals
  • Proven ability to contribute to architectural planning
  • Familiarity with audio/text/video multi-modal frameworks
  • Experience implementing real-time inference pipelines
  • Working knowledge of latent diffusion audio models
  • Strong knowledge of industry-standard audio datasets
  • Experience optimizing inference pipelines for creative applications
  • Proficiency in lower-level audio frameworks
  • Contributions to published research at top-tier conferences
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The Company
HQ: Burbank, CA
219,548 Employees
Year Founded: 1923

What We Do

The Walt Disney Company is a leading diversified international family entertainment and media enterprise that operates through segments including entertainment, sports, and experiences.

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