Computer Vision Engineer

Role Overview: You will be a Computer Vision Engineer at RNT Health Insights, playing a crucial role in developing and implementing spatiotemporal algorithms for real-time lesion detection during endoscopic procedures. Your work will involve collaborating with a diverse team to enhance spatial and temporal accuracy in lesion detection, designing preprocessing pipelines, optimizing models, and ensuring high-performance deployment in clinical settings. Key Responsibilities: - Research, develop, and implement spatiotemporal techniques combined with CNN and other spatial models for real-time lesion detection in endoscopic video streams. - Investigate and apply state-of-the-art techniques like LSTMs and 3D CNNs to model temporal dependencies for improved lesion tracking and detection. - Integrate temporal models with existing CNN-based spatial models to create efficient end-to-end pipelines for real-time inference during endoscopy. - Design and implement robust video preprocessing and inference pipelines for smooth integration with endoscopic hardware and software systems. - Work on post-processing techniques to enhance lesion localization, classification, and segmentation accuracy, ensuring consistent performance in various clinical settings. - Fine-tune models for deployment on constrained hardware platforms, maintaining low-latency performance without compromising accuracy. - Collaborate with research teams to explore cutting-edge temporal models, multi-frame fusion techniques, and domain-specific innovations in medical video analysis. - Benchmark models against datasets, assess performance metrics, and optimize for real-time clinical use. Qualifications Required: - Bachelor's/ Master's/ PhD in Computer Science, Electrical Engineering, or related fields with a focus on Artificial Intelligence, Machine Learning, or Computer Vision. - Strong foundation in computer vision principles, including image processing techniques, feature extraction methodologies, and neural network architectures. - Proficiency in deep learning frameworks such as TensorFlow, Keras, or PyTorch. - Proficient in programming languages, particularly Python and C++, capable of writing clean, efficient, and well-documented code. - Solid understanding of machine learning algorithms, real-time inference, and model optimization for deployment in resource-constrained environments. - Ability to engage with scientific literature, staying updated on advancements in computer vision and machine learning for medical applications. - Effective communication skills, both oral and written, and the ability to work well in a team environment, giving and receiving feedback openly. If you are passionate about computer vision and excited to contribute to cutting-edge healthcare technology, RNT Health Insights would like to hear from you!,