Responsibilities:

• Design, train, and deploy deep neural networks for audio and video applications (e.g. noise/echo cancellation, beamforming, action classification)
• Prototype algorithms in MATLAB/Python and implement them efficiently in C/C++ for embedded systems
• Research state-of-the-art architectures for multimodal representation learning (e.g. audio–video fusion, self-supervised pretraining, contrastive learning) and implement on edge devices such as Android systems
• Document designs, tests, and releases, and communicate technical challenges across teams

Requirements:

• Bachelor’s or Master’s in Electrical Engineering, Computer Science or related field with relevant experience
• Open to outstanding fresh PhD graduates
• Strong background in machine learning and deep learning for signal processing
• Proficiency in C/C++ for embedded systems and experience with real-time operating systems, familiarity with ARM/NEON or SIMD optimization is a plus
• Experience with data pipelines, training, evaluation, and experiment tracking
• Familiarity with popular audio framework (Audio HAL, ALSA) is a plus
• Familiarity with YOLO, SLAM, ViT is a plus