Senior Applied Machine Learning Researcher - Generative Models

Summary

Join Splice, a creative platform for music makers, as an exceptional Applied Researcher. You will conduct generative AI research focusing on Latent Diffusion and Transformer-based architectures for audio and symbolic music generation. Responsibilities include model development, prototyping, collaboration with ML engineers and external communities, and documentation. A PhD or Master's degree in a related field, along with proven experience in applied research (2+ years) and proficiency in Python and deep learning frameworks, is required. Splice offers a remote-work culture, competitive compensation and equity, unlimited PTO, comprehensive health benefits, retirement savings, parental leave, and professional growth opportunities.

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
• 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
• 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
• 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

Preferred Qualifications

• 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

Benefits

• Competitive pay with annual reviews and equity opportunities
• Unlimited PTO to recharge and thrive
• Comprehensive medical, dental, and vision coverage for you and your dependents
• 401(k) plan with immediate vesting and company match
• 12 weeks of fully paid parental leave for non-birthing parents, and 18-20 weeks for birthing parents
• Work remotely or connect at our office hubs and creative spaces worldwide
• Annual learning budget, leadership programs, and team ambassador opportunities
• Inclusive events, team meet-ups, and vibrant Employee Resource Groups