Computer Vision Engineer

As a Computer Vision Engineer at RNT Health Insights, you will be at the forefront of researching, developing, and implementing spatiotemporal algorithms for detecting and delineating lesions in real-time during endoscopic video feeds. Your role will involve collaborating with a multidisciplinary team of AI researchers, gastroenterologists, and software engineers to enhance both spatial and temporal accuracy in lesion detection. You will be responsible for designing preprocessing pipelines, optimizing models, and ensuring low latency, high-performance deployment in clinical settings. Key Responsibilities - Algorithm Development: Research, develop, and implement spatiotemporal techniques combined with CNN and other spatial models for real-time lesion detection in endoscopic video streams. - Temporal Analysis: Investigate and apply state-of-the-art techniques (e.g., LSTMs, 3D CNNs) to model temporal dependencies in video-based data for improved lesion tracking and detection. - Model Integration: Integrate temporal models with existing CNN-based spatial models to create efficient, end-to-end pipelines for real-time inference during endoscopy procedures. - Preprocessing & Inference Pipelines: Design and implement robust video preprocessing (e.g., frame extraction, image enhancement, noise reduction) and inference pipelines that ensure smooth integration with endoscopic hardware and software systems. - Post-processing Optimization: Work on post-processing techniques to improve lesion localization, classification, and segmentation accuracy, and ensure consistent performance in different clinical settings. - Model Optimization: Fine-tune models for deployment on constrained hardware platforms, ensuring low-latency performance without compromising accuracy. - Collaborative Research: Collaborate with research teams to explore and incorporate cutting edge temporal models, multi-frame fusion techniques, and domain-specific innovations in medical video analysis. - Performance Benchmarking: Benchmark models against datasets, assess performance (accuracy, speed, robustness), and optimize for real-time clinical use. Qualifications And Expectations - Bachelor's/ Master's/ PhD in Computer Science, Electrical Engineering, or closely 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 with deep learning frameworks such as TensorFlow, Keras, or PyTorch. - Proficiency in programming languages, particularly Python and C++, with the ability to write clean, efficient, and well-documented code. - Solid grasp of machine learning algorithms and methodologies, including experience with real-time inference and model optimization for deployment in resource-constrained environments. - Ability to engage with and interpret relevant scientific literature, staying updated on the latest advancements in computer vision and machine learning as they apply to medical applications. - Ability to communicate ideas clearly and effectively, both in oral and written formats. - Self-motivated individual who works well in a team, and is open to giving and receiving honest feedback. If you are passionate about computer vision and eager to contribute to cutting-edge healthcare technology, we would love to hear from you!,