Overview

Job Title: Computer Vision Engineer

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

• 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