- Implementing novel deep neural network architectures and learning techniques to solve a variety of audio related tasks and push the state of the art in performance.
- Research and develop advanced audio applications including speech recognition, voice recognition, voice wakeup using state of art deep learning algorithms.
- Research and develop advanced audio signal processing algorithms including noise cancellation, localization, acoustic detection, acoustic fingerprinting.
- Analyze and optimize audio signal processing and deep learning algorithms on mobile/embedded devices, e.g., using hardware acceleration such as GPU/DSP.
- M.S. in Computer Science, Machine Learning, Electrical Engineering, Robotics or similar field (Ph.D. is preferred).
- Solid understanding on audio signal processing.
- Solid understanding and experience of applying deep learning to real problems in the fields of voice recognition, speech recognition, and other audio related applications.
- Holistic understanding of deep learning concepts, state of the art in audio processing research and the mathematics of machine learning. Familiar with CNN, RNN, LSTM.
- Strong experience in C/C++ programing.
- Hands-on experience in 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.
- 3+ years of industry experience with deep learning algorithm development and optimization.
- 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, is a strong plus.