Sr. Applied Machine Learning Researcher - Generative Models
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THE ROLE:
We are seeking an exceptional Applied Researcher with experience in Generative models for audio using Latent Diffusion, as well as symbolic music generation techniques based on Transformer architectures. Solid experience and track record in only one of the two areas would be considered. The ideal candidate will bring a research-focused mindset with practical application skills, translating state-of-the-art techniques to novel, usable and performant designs and solutions. At Splice, we believe that Generative AI has the potential to augment and extend the sonic boundaries of our human-made, world class catalog, and bring powerful unlocks to our users’ creative workflow.
TEAM INFORMATION:
The Splice AI & Audio Science team is dedicated to pushing the boundaries of artificial intelligence applied to audio data, with the mission to empower music creators everywhere. Being musicians ourselves, we are deeply committed to the use of AI in a creator-centric, ethical and responsible way. Our team consists of passionate and creative individuals who thrive in a collaborative, innovative, and fast-paced environment.
WHAT YOU’LL DO:
Generative AI Research: Conduct literature research and experimentation in the field of ML-based generative audio using Latent Diffusion and symbolic music generation using Transformer-based architectures.
Model development: 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. Explore, adapt and implement core building blocks for generative models, such as general Variational Autoencoders (VAEs), Neural Audio Codecs (RVQ / VAE), GANs, Diffusion Models, and Transformer-based architectures.
Prototyping: Develop proof-of-concept prototypes to showcase and validate capabilities and use cases using generative audio/symbolic models. Iterate and refine models based on quantitative/qualitative feedback and evaluation metrics.
Collaboration: 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.
Documentation and Knowledge Sharing: Document research findings, methodologies, and best practices. Collaborate with team members to disseminate knowledge and insights.
JOB 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.
NICE TO HAVES:
A relevant portfolio of research projects, publications, or open-source contributions related to generative audio.
Prior experience in machine learning model optimization.
Background or knowledge in music production.
The national pay range for this role is $165,000 - $206,000. Individual compensation will be commensurate with the candidate's experience.
About the job
Sr. Applied Machine Learning Researcher - Generative Models
To see similar active jobs please follow this link: Remote Development jobs
THE ROLE:
We are seeking an exceptional Applied Researcher with experience in Generative models for audio using Latent Diffusion, as well as symbolic music generation techniques based on Transformer architectures. Solid experience and track record in only one of the two areas would be considered. The ideal candidate will bring a research-focused mindset with practical application skills, translating state-of-the-art techniques to novel, usable and performant designs and solutions. At Splice, we believe that Generative AI has the potential to augment and extend the sonic boundaries of our human-made, world class catalog, and bring powerful unlocks to our users’ creative workflow.
TEAM INFORMATION:
The Splice AI & Audio Science team is dedicated to pushing the boundaries of artificial intelligence applied to audio data, with the mission to empower music creators everywhere. Being musicians ourselves, we are deeply committed to the use of AI in a creator-centric, ethical and responsible way. Our team consists of passionate and creative individuals who thrive in a collaborative, innovative, and fast-paced environment.
WHAT YOU’LL DO:
Generative AI Research: Conduct literature research and experimentation in the field of ML-based generative audio using Latent Diffusion and symbolic music generation using Transformer-based architectures.
Model development: 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. Explore, adapt and implement core building blocks for generative models, such as general Variational Autoencoders (VAEs), Neural Audio Codecs (RVQ / VAE), GANs, Diffusion Models, and Transformer-based architectures.
Prototyping: Develop proof-of-concept prototypes to showcase and validate capabilities and use cases using generative audio/symbolic models. Iterate and refine models based on quantitative/qualitative feedback and evaluation metrics.
Collaboration: 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.
Documentation and Knowledge Sharing: Document research findings, methodologies, and best practices. Collaborate with team members to disseminate knowledge and insights.
JOB 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.
NICE TO HAVES:
A relevant portfolio of research projects, publications, or open-source contributions related to generative audio.
Prior experience in machine learning model optimization.
Background or knowledge in music production.
The national pay range for this role is $165,000 - $206,000. Individual compensation will be commensurate with the candidate's experience.