Senior AI and Deep Learning Architect – Model Compression and Quantization
Job description
- Research and develop state-of-the-art model compression techniques including QAT, model distillation, pruning, quantization, model binarization, and others for deep learning models.
- Implementing novel deep neural network architectures and developing advanced training algorithms to support model structure training, auto pruning and low-bit quantization.
- Apply and optimize model compression and quantization technique to variety of models in computer vision applications, audio applications, and others.
- Research and optimize model compression and quantization technique for Kneron AI accelerator and jointly optimize hardware architecture for compressed model.
- Intern or full time.
Requirements
- M.S./PhD in Computer Science, Machine Learning, Mathematics or similar field (Ph.D. is preferred)
- 3+ years of industry/academia experience with deep learning algorithm development and optimization.
- 3-5 years of software engineering experience in an academic or industrial setting.
- Research experience on any model compression and model quantization technique including model distillation, pruning, post train quantization, quantization aware retrain, model binarization, and NAS.
- Experience on model accuracy loss analysis for model compression and quantization is a strong plus. Noise modeling and noise analysis are strong plus.
- Strong experience in C/C++ programing is a plus.
- Hands-on experience in computer vision and deep learning frameworks, e.g., OpenCV, Tensorflow, Keras, Pytorch, and Caffe.
- Ability to quickly adapt to new situations, learn new technologies, and collaborate and communicate effectively.
- Experience with parallel computing, GPU/CUDA, DSP, and OpenCL programming is a plus.
- Top-tier conference publication records, including but not limited to CVPR, ICCV, ECCV, NIPS, ICML, are strong plus.
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