Prototyping Technician (Software Engineer C++)

Role: Prototyping Technician(Software Engineer C++)

Location: Zurich, Switzerland

100% onsite role.

Skills required: C++(experience with latest version like C++17 or C++20, Qt or open GL, algorithms, data structures, master’s degree, linux

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.

Other Desired Skills:

• Extensive 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.
• Efficiency in Python, command line Python scripting, and explorative work with Python notebooks, using modules such as numpy, scipy, matplotlib, etc.
• Knowledge of CV and relevant libraries such as OpenCV.
• Knowledge of CG and relevant standards such as OpenGL and their implementations, such as Qt.
• Knowledge of game development engines such as Unity, Unreal.
• Knowledge of Android development and debugging, ideally in the context of XR on mobile and HMDs.
• 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).