About this role
We are seeking a Computer Vision Engineer with deep theoretical foundations and extensive experience in classical computer vision to join our space systems team in Luxembourg. This role requires a strong mathematical and optical background to solve complex engineering challenges on resource-constrained satellite payloads. You will design, prototype, test, verify, and validate algorithms embedded directly into spaceborne electronics for Space Domain Awareness (SDA), Space Situational Awareness (SSA), and Rendezvous and Proximity Operations (RPO) applications. Collaborating closely with multidisciplinary hardware and flight software teams, you will ensure full flight readiness, performance, and compliance with mission requirements. Key Responsibilities: • Architecting computer vision solutions (algorithm trade-offs, design, selection) • Prototyping, implementing, testing, validating, and verifying your solution based on the target hardware (either embedded space electronics or standard electronics). • Supporting Flight Software and Hardware Engineers to embed your solution. • Supporting final integration, testing, validation and qualification activities. • Preparing, writing and reviewing technical documentation, specifications, designs of your solution. Experience & Qualifications: • Background: Degree in Computer Science, Physics, Mathematics, Optics, or a related technical field. • Work Experience: 5+ years of work experience in computer vision applications. • Classical CV Mastery: Strong background in non-learning computer vision techniques using OpenCV. Proficient in fundamental algorithms including spatial filtering, morphological transformations, thresholding, edge detection, and feature matching applied to real-world edge/embedded hardware. • Theory: Great knowledge of the fundamental optical concepts underpinning computer vision discipline: optics, image formation, image manipulation, radiometry, etc. Good understanding of detectors' electro-optical properties and managing images at the pixel level. • Data Science & ML: Proven experience applying traditional supervised and unsupervised machine learning models to spatial, temporal, and cluster-based datasets. • Autonomy: Strong ability to work autonomously on your own initiative and without direct supervision • Communication: Excellent English proficiency with the ability to write and manage standard documentation, software documentation, and scientific documentation. • Nationality: EU nationality is mandatory due to project requirements. Assets (Nice to have): • Experience in space system, space applications. • Any experience with star trackers, Earth observation payloads or in general optical sensor payloads • Experience in photogrammetry. • Experience in embedded systems. • Proficiency in C++ programming