Expertise and intuition for training models in the audio domain, including text-to-speech, ASR, speech-to-speech, speech-emotion-recognition, or other models
Experience in training audio autoencoders
Understanding of signal processing, especially of audio signals
Experience with diffusion models, consistency models, or GANs
Experience with training on large-scale (multi-node) GPU clusters
Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
Understanding of and interest in large-scale, highly parallel data processing pipelines
Proficiency with PyTorch and Python
Experience contributing to large pre-existing codebases and rapidly getting up to speed
Previously published machine learning research in well-respected venues
Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning)
