Senior Staff R&D Engineer

What you'll do

• Lead the research and implementation of machine learning algorithms for speech processing, voice transfer, source separation, and upmixing.
• Architect and deploy scalable model training pipelines using PyTorch and distributed computing frameworks.
• Develop generative audio models including latent diffusion, flow-based models, VAEs, and neural vocoders.
• Manage the end-to-end model lifecycle including pretraining, fine-tuning, validation, inference optimization, and CI/CD integration.
• Create personalized model adaptation workflows to support per-user tuning and cross-project continuity.
• Define integration strategies for audio models within a secure, cloud-optimized SaaS environment.
• Translate emerging research in generative audio and multi-modal modeling into production-ready innovations.
• Develop internal tools and infrastructure to improve the iteration speed and reproducibility of deployed models.

What we're looking for

• Master's or PhD in Computer Science, Electrical Engineering, Applied Math, or a related field focused on AI/ML and multi-modal signal processing.
• At least 5 years of professional experience in applied ML with a focus on audio-centric research and deployment.
• Expertise in building and scaling models using PyTorch for deep neural network training, fine-tuning, and inference.
• Demonstrated experience developing generative models such as VAE, GAN, diffusion models, or neural vocoders.
• Deep understanding of audio-specific ML domains including source separation, speech enhancement, and music processing.
• Proficiency in audio signal processing fundamentals and integrating DSP into machine learning pipelines.
• Experience with MLOps tooling, Docker containerization, and scalable infrastructure for distributed training.
• Ability to contribute to architectural planning, research strategy, and production deployment in complex environments.