Headquartered in the United States, TP-Link Systems Inc. is a global provider of reliable networking devices and smart home products, consistently ranked as the world's top provider of Wi-Fi devices. The company is committed to delivering innovative products that enhance people's lives through faster, more reliable connectivity. With a commitment to excellence, TP-Link serves customers in over 170 countries and continues to grow its global footprint. We believe technology changes the world for the better! At TP-Link Systems Inc, we are committed to crafting dependable, high-performance products to connect users worldwide with the wonders of technology. Embracing professionalism, innovation, excellence, and simplicity, we aim to assist our clients in achieving remarkable global performance and enable consumers to enjoy a seamless, effortless lifestyle. Job Description We are looking for an AI/ML Computer Vision Engineer (Entry Level) to support the development and deployment of AI-powered features within our smart home automation products, including smart security cameras, video doorbells, and autonomous vacuum cleaners. In this role, you'll assist in optimizing real-time machine learning inference and video analytics. The ideal candidate is eager to learn and has foundational knowledge of embedded AI, computer vision, and real-time video processing, with practical experience or academic exposure to deploying ML models on edge devices. Additionally, interest or experience in training and deploying lightweight LLMs (Large Language Models) on edge hardware is a strong plus. Responsibilities * Assist in the development and implementation of computer vision pipelines for real-time object detection, tracking, and classification. * Support integration of multi-sensor fusion techniques, including video, audio, radar, and LiDAR data, to enhance device functionality. * Participate in optimization of deep learning models for inference on embedded hardware platforms (e.g., TensorFlow Lite, ONNX Runtime, OpenVINO, NVIDIA Jetson, Coral Edge TPU). * Help apply quantization, pruning, and model compression methods to optimize performance on resource-constrained edge devices. * Collaborate with senior team members and cloud developers to support edge-to-cloud data pipeline integration.