Senior Machine Learning Engineer, Splice

• Master's or PhD degree in Electrical Engineering, Computer Science or related Engineering discipline
• Proven ability and track record designing, training, evaluating and deploying machine learning models in production environments, powering real applications
• 2+ years of hands-on experience with generative models architectures in the audio, image or language domains. Specific experience with Latent Diffusion Models and Transformer-based architectures is a must
• Proficiency in Python, C/C++, or CUDA. Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch)
• Hands-on experience with cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes)
• Comfortable with software development best practices and version control systems (e.g., Git)

Desirable

• 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
• Design, adapt and optimize cutting-edge model architectures for generative audio/music applications, leveraging state-of-the-art deep learning techniques for audio/music synthesis
• Collaborate with other Applied Researchers and Machine Learning Engineers to design, train, fine-tune, and deploy scalable models to production
• Explore and implement core building blocks in generative models, such as general Variational Autoencoders (VAEs), Neural Audio Codecs (RVQ / VAE), GANs, Diffusion Models, and Transformer-based architectures
• Contribute to integrating machine learning models into Splice’s products, delivering new and creative experiences for music creators
• Performance Benchmarking and Evaluation**:** design and run experiments to benchmark the accuracy, quality and performance of trained models
• Stay current with the latest advancements in machine learning applied to generative models in the audio domain, incorporating and sharing relevant insights into the applied research process
• Documentation and Knowledge Sharing**:** document experiments, best practices, and lessons learned to facilitate knowledge sharing and maintain reproducibility. Provide technical guidance and training to team members on model training, evaluation, deployment and optimization techniques