Computer Vision Engineer (m/f/d)

The successful candidate will be responsible for developing end-to-end perception pipelines, optimizing machine learning models for edge deployment, and ensuring reliable performance under challenging conditions such as occlusion, varying lighting, motion, environmental interference, and complex multi-object scenarios.Key ResponsibilitiesDesign, develop, train, and optimize computer vision and machine learning models for object detection, classification, recognition, and tracking. Develop robust multi-object tracking and re-identification solutions capable of maintaining continuity through occlusions, rapid movement, environmental challenges, and sensor variations. Create object selection, prioritization, and decision-making algorithms for complex scenes containing multiple targets or points of interest. Optimize machine learning models for deployment on embedded and edge computing platforms, ensuring efficient operation within real-time processing and resource constraints. Define, monitor, and improve key performance indicators including detection accuracy, tracking stability, false alarm rates, response latency, and target reacquisition performance. Develop and maintain data collection, annotation, augmentation, and dataset management processes to support model development and continuous improvement. Build simulation, validation, and testing environments to evaluate algorithm performance across diverse operational scenarios. Design interfaces between perception components and downstream software systems, ensuring accurate transfer of object state information, confidence metrics, timestamps, and spatial data. Support system integration, verification, validation, and performance testing activities. Conduct root-cause investigations, performance analysis, and model tuning to address edge cases and challenging operating conditions. Research and evaluate emerging computer vision, machine learning, and artificial intelligence techniques to improve system capabilities. Produce technical documentation covering algorithms, model architecture, performance results, validation activities, limitations, and deployment strategies. RequirementsStrong proficiency in Python and C++ with extensive experience developing production-quality software applications. Deep knowledge of computer vision, machine learning, and deep learning methodologies. Hands-on experience with object detection, object classification, segmentation, and multi-object tracking algorithms. Strong experience with modern deep learning frameworks such as PyTorch, TensorFlow, or equivalent technologies. Experience optimizing and deploying machine learning models on embedded, edge, GPU-accelerated, FPGA-based, or specialized AI hardware platforms. Working knowledge of image processing techniques including image enhancement, denoising, stabilization, super-resolution, and feature extraction. Familiarity with multiple sensor technologies, including visible-light, thermal, infrared, and other imaging systems. Experience evaluating model performance using quantitative metrics and validation methodologies. Understanding of real-time perception systems and low-latency processing requirements. Experience with software development best practices, including version control, testing frameworks, continuous integration, and code reviews. Strong analytical, troubleshooting, and problem-solving capabilities. Ability to work independently while collaborating effectively within multidisciplinary engineering environments. Excellent written and verbal communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. QualificationsBachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Robotics, Data Science, or a related technical discipline. Minimum of 6 years of hands-on experience in computer vision, machine learning, artificial intelligence, or related fields. Demonstrated experience developing and deploying real-world perception systems for robotics, automation, intelligent devices, industrial systems, autonomous platforms, or similar technology domains. Strong understanding of end-to-end perception architectures, sensor integration, real-time data processing, and edge-computing environments. Experience with large-scale dataset development, annotation workflows, model training pipelines, and performance optimization. PhD in a relevant field is considered an advantage. Preferred ExperienceReal-time object detection and tracking systems. Multisensor and sensor-fusion applications. Edge AI and embedded machine learning deployment. Robotics, automation, intelligent systems, or autonomous technologies. Performance optimization for low-latency and resource-constrained environments. Large-scale machine learning model development and operational deployment. Computer Vision Engineer in Abu Dhabi, United Arab Emirates