Ideal candidate:

  • Authored or contributed to key generative audio projects (WaveNet, AudioLM, Tacotron, Tortoise, AudioCraft, MusicGen, fairseq, wav2vec, etc.)
  • Experience training and implementing inference for transformers and diffusion models
  • Proficient in Python
  • Proficient in at least one of PyTorch, Tensorflow, or JAX

Technologies:

  • PyTorch, TorchAudio
  • Numpy/Scipy, Jupyter, Pandas
  • CUDA / C++

Preferred:

  • CUDA performance nut
  • MLOps experience (Ray, KServe, Triton, Seldon)
  • Experience with Kubernetes
  • Experience designing distributed system architectures able to handle high traffic and concurrency
  • Audio engineering background

Apply Here