Computer Vision Engineer

We are seeking an experienced Computer Vision & OCR Engineer to design, develop, and optimize advanced visual recognition systems focused on optical character recognition, image understanding, document analysis, and machine-readable extraction from complex visual data. The ideal candidate has strong experience developing computer vision and image-processing algorithms and a deep understanding of OCR, pattern recognition, image enhancement, geometric analysis, feature extraction, and visual perception. This role will involve taking computer-vision concepts from research and algorithm development through testing, optimization, and production deployment.The engineer will work on challenging real-world visual recognition problems involving text, symbols, numbers, structured documents, and other visually encoded information. The position requires strong software engineering ability combined with a rigorous understanding of how images can be transformed into reliable, explainable, and machine-readable information.Key ResponsibilitiesDesign, develop, and optimize computer vision algorithms for real-world visual recognition applications.Develop image-processing pipelines for image enhancement, normalization, noise reduction, geometric correction, segmentation, feature extraction, and visual analysis.Develop algorithms for detecting, locating, isolating, and interpreting characters, symbols, numbers, and other visual features.Analyze complex images and develop robust methods for extracting meaningful information from varying image quality, scale, orientation, lighting, distortion, and perspective.Develop techniques for image registration, alignment, calibration, transformation, and geometric analysis.Evaluate and improve computer-vision algorithms using quantitative performance measurements and representative datasets.Investigate failure cases and develop algorithmic improvements to increase recognition reliability and robustness.Design, develop, and optimize OCR and document-recognition systems.Develop algorithms for character, number, and symbol recognition from scanned documents, photographs, screenshots, forms, labels, equipment markings, and other visual sources.Develop preprocessing and segmentation techniques specifically designed to improve OCR performance.Analyze character morphology, geometry, spatial relationships, contours, strokes, connected components, and other visual characteristics relevant to recognition.Develop methods for locating text regions and separating individual characters or symbols from complex backgrounds.Build systems capable of processing text under challenging conditions including rotation, perspective distortion, low resolution, noise, compression artifacts, variable illumination, and non-uniform backgrounds.Develop recognition confidence and validation mechanisms that distinguish between strong visual evidence and ambiguous or insufficient evidence.Benchmark OCR performance against established OCR systems and relevant industry datasets.Analyze recognition errors at the character, word, field, and document levels and develop methods to systematically reduce error rates.QualificationsStrong foundation in Computer Vision and Pattern Recognition, with experience designing and evaluating visual algorithms.Background in Computer Science, including proficiency in data structures, algorithms, and software engineering best practices.Experience in Data Science, such as data preprocessing, model evaluation, and quantitative analysis of system performance.Exposure to Robotics or autonomous systems, including integrating perception modules with control or navigation pipelines.Proficiency in one or more programming languages commonly used in vision and robotics (e.g., Python, C++, MATLAB) and familiarity with relevant libraries (e.g., OpenCV, PCL).Understanding of geometric methods, linear algebra, and optimization as applied to visual reasoning and 3D perception.Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related technical field; equivalent practical experience also considered.Ability to work in a hybrid environment, communicate clearly with technical and non-technical stakeholders, and collaborate within a multidisciplinary team.