Software Engineer - Computer Vision Engineer
Qualitest acqSwitzerland2 days ago
Full-timeEngineering, Information Technology
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Zurich, Switzerland (100% On-site)
Responsibilities
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).
- 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.
- 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).
Key Skills
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