We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning, including end-to-end voice interactions, natural language processing, and combining learning with knowledge.
Minimum Qualifications
Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying deep learning techniques to products
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
Ability to work in a collaborative environment.
PhD, or equivalent practical experience, in Computer Science, or related technical field.
Preferred Qualifications
Code Large language models
Experience in building end-to-end voice models
Experience in speech recognitions and generations
Experience in reinforcement learning, on-policy distillation
Experience in post-training, mid-training large language models