Senior AI and Deep Learning Architect – Model Compression and Quantization

Job description

  1. Research and develop state of art model compression techniques including model distillation, pruning, quantization, model binarization, and others for CNN, RNN, LSTM models.
  2. Implementing novel deep neural network architectures and develop advanced training algorithm to support model structure training, auto pruning and low-bit quantization.
  3. Apply and optimize model compression technique to variety of models in computer vision applications, audio applications, and others.  
  4. Research and optimize model compression technique for Kneron CNN accelerator and jointly optimize hardware architecture for compressed model.  


  1. M.S./PhD in Computer Science, Machine Learning, Mathematics or similar field (Ph.D. is preferred)
  2. .3+ years of industry/academia experience with deep learning algorithm development and optimization.
  3. 3-5 years of software engineering experience in an academic or industrial setting.
  4. Holistic understanding of deep learning concepts, state of the art in model compression research and the mathematics of machine learning.  
  5. Solid understanding of CNN, RNN, LSTM, variety of training method, learning rate choice, hyper-parameter tuning.
  6. Research experience on any model compression technique including model distillation, pruning, quantization, model binarization.
  7. Strong experience in C/C++ programing.
  8. Hands-on experience in computer vision and deep learning frameworks, e.g., OpenCV, Tensorflow, Keras, Pytorch, and Caffe.
  9. Experience on hardware architecture design is a plus.  
  10. Ability to quickly adapt to new situations, learn new technologies, and collaborate and communicate effectively.
  11. Experience with parallel computing, GPU/CUDA, DSP, and OpenCL programming is a plus.
  12. Top-tier conference publication records, including but not limited to CVPR, ICCV, ECCV, NIPS, ICML, is a strong plus.


Taipei/Hsinchu/USA_San Diego/Shenzhen/Zhuhai


If interested, please send your resume to: /