Responsibilities:

• Design, train, and deploy deep neural networks for video, audio, rPPG (remote photoplethysmography), and LLM applications (e.g. noise/echo cancellation, beamforming, action classification, RAG, physiological signal extraction)

• 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, video-based physiological signal estimation/rPPG) and implement on edge devices such as Linux & Android systems

• Document designs, tests, and releases, and communicate technical challenges across teams

Requirements:

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science or a related field with relevant experience

• Open to outstanding fresh PhD graduates

• Strong background in machine learning and deep learning for signal processing

• Experience with rPPG (remote photoplethysmography) or video-based physiological signal extraction using deep learning is highly desirable

• 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

• Candidate with extensive experience would be considered as Senior AI Engineer