Computer Vision Failure Analysis Engineer
Santa Clara Valley (Cupertino), California, United States
Machine Learning and AI
The Video Computer Vision org is an applied research and engineering organization responsible for developing real-time on-device Computer Vision and Machine Perception technologies across Apple products. We balance research and product to deliver Apple quality, state-of-the-art experiences, innovating through the full stack, and partnering with HW, SW and ML teams to influence the sensor and silicon roadmap that brings our vision to life.
- - Extensive background in statistics, data analysis, and data visualization
- - In depth experience with failure analysis of image processing/computer vision algorithms
- - Ability to analyze and debug complex algorithms
- - Highly skilled in at least one scripting language such as Python or Matlab and proven experience in C++
- - Good understanding and applied experience in classic 2D image processing and segmentation including:
- - Robust semantic object detection under different lighting conditions
- - Segmentation of non-rigid contours in challenging/low contrast scenarios
- - Sub-pixel accurate refinement of contours and features
- - Experience with 3D reconstruction algorithms
- - Experience in image quality assessment (e.g. SNR, MTF, compression analysis)
We work on complex problems in computer vision that require robust, efficient, well tested, and clean solutions. The ideal candidate will possess the self-motivation, curiosity, and initiative to achieve those goals. Analogously, the candidate is a lifelong learner who passionately seeks to improve themselves and the quality of their work. You will work together with similar minds in a unique development team where your skills and expertise can be used to influence future user experiences and hardware that will be used by millions.
Education & Experience
A bachelor's degree or higher in Applied Mathematics, Data Science, Computer Science or equivalent field, plus 5 years industry experience or a PhD / Post doc.