Sr. Applied Machine Learning Researcher - Generative Models (US Remote)

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Requirements:

• Ph.D. or Master's degree in Electrical Engineering, Computer Science or related Engineering discipline.
• Background or proven experience in Digital Signal Processing.
• Proven experience (2+ years) in an Applied Research role focused on Latent Diffusion based generative models for audio and/or symbolic music generation using Transformer-based architectures.. Alternatively, solid experience with diffusion-based models in the image domain, would be considered.
• Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with software development best practices and version control systems (e.g., Git).
• Strong communication and collaboration skills, with the ability to work cross-functionally with stakeholders in Engineering, Product and Design.

Responsibilities:

• Conduct literature research and experimentation in the field of ML-based generative audio using Latent Diffusion and symbolic music generation using Transformer-based architectures.
• Collaborate with our ML Engineers to design performant model architectures for efficient ML-based audio synthesis and symbolic music generation, as well as adapting and fine-tuning existing models.
• Develop proof-of-concept prototypes to showcase and validate capabilities and use cases using generative audio/symbolic models.
• Engage with academic and open source communities to stay up to date with the latest developments in the space, collaborate in joint projects, and identify top talent for our AI & Audio Science team’s future hiring needs.
• Stay up-to-date with the latest academic and industrial research in generative models for music, incorporating relevant findings into our applied research and product development processes.
• Document research findings, methodologies, and best practices. Collaborate with team members to disseminate knowledge and insights.