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. Responsibilities • 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…