• Design, develop, and optimize computer vision models for real-time applications on embedded systems • Implement model compression techniques such as quantization, pruning, and knowledge distillation to improve performance on low-power hardware • Deploy machine learning models on embedded platforms, including ARM, NVIDIA Jetson, Qualcomm, or custom ASICs • Write clean, efficient, and well-documented code in Python and C++, leveraging ML frameworks like TensorFlow, PyTorch, and ONNX • Develop and fine-tune SLAM, object detection, tracking, and feature extraction models for high efficiency • Collaborate with cross-functional teams to integrate ML models into production systems, optimizing for latency, accuracy, and power consumption • Benchmark and profile ML models to identify and implement optimizations for inference on embedded hardware • Research and apply cutting-edge ML techniques to improve real-time performance in resource-constrained environments