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Software Engineer - Computer Vision Engineer Date: 9 Dec 2025 Company: Qualitest Group Country/Region: CH Software Engineer - Computer Vision Engineer Zurich, Switzerland (100% On-site) Responsibilities: - Our team of Engineers will be responsible for contributing to daily work on building software products and features shipping on the HMDs. - Engineers are not expected to design and lead the implementation of complete features; however, a substantial degree of independence and self-drive are required to minimize onboarding and supervision. - Example tasks - Debugging and upgrading tools used and/or implemented by the team. Evaluating performance implications (CPU, GPU, memory, thermal impact) of features deployed to HMDs. - Developing and/or improving tools for visualizing data collected from the HMDs. - Contributing to improved code quality by participating in code reviews, design document reviews, etc, implementing extensive tests, etc. - Participating in on-call rotations, mitigating and resolving incidents, writing postmortems. - Preferred Skills-Solid engineering skills in the relevant programming languages and operating systems (C++, Python, Linux, Android). Knowledge in at least one of the following domains: - Computer Vision (CV), Virtual/Augmented/Mixed Reality (XR), Computer Graphics (CG), Machine Learning (ML) - Experience in a fast-paced technology environment with extensively cross-functional (XFN) work and be able to thrive in ambiguity. - Willingness to dive into data related issues, applying manual QA when needed. Other Desired Skills: - Experience working with cameras, semantic/instance/panoptic segmentation-Knowledge of stereo depth - Proficiency in Python, command line Python scripting, and explorative work with Python notebooks, using modules such as numpy, scipy, matplotlib, etc. - Hands-on knowledge of C++, ideally with good familiarity of the recent standards (C++17, C++20) and corresponding standard libraries. - Good understanding of advanced template metaprogramming. - Good knowledge of C++ testing with libraries such as Googletest. - Familiarity with the Buck build tool. - Knowledge of CV and relevant libraries such as OpenCV. - Knowledge of CG and relevant standards such as OpenGL and their implementations, such as Qt. - Practical knowledge of ML, neural networks, and deep learning, with focus on developing and using infrastructure for deploying models in production (ML Engineering, ML Ops).
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