Software Engineer - Computer Vision Engineer

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).