Senior Staff R&D Engineer

What you'll do

• Lead the research, design, and implementation of ML algorithms for speech processing, voice transfer, and source separation.
• Architect and deploy scalable model training pipelines using PyTorch and distributed computing frameworks.
• Develop novel generative audio models including latent diffusion, flow-based models, and neural vocoders.
• Manage the end-to-end model lifecycle including pretraining, fine-tuning, validation, and inference optimization.
• Create personalized model adaptation workflows to support per-user tuning and cross-project continuity.
• Define integration strategies for deploying models within a secure, cloud-optimized SaaS environment.
• Build internal tooling and infrastructure to improve iteration speed and reproducibility of deployed models.
• Mentor junior researchers and engineers to foster a culture of rigorous experimentation 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 multi-modal 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, including training, fine-tuning, and inference for deep neural networks.
• 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, music processing, and cross-modal tasks.
• Experience with MLOps tooling, Docker-based containerization, and scalable infrastructure for distributed training.
• Fluency in audio signal processing fundamentals and the integration of DSP into ML pipelines.
• Familiarity with multi-modal frameworks, real-time inference pipelines, and lower-level audio frameworks like C or C++ (preferred).