If you are the kind of people who are passionate on pursuing excellence, embracing challenges, enjoying work with others, learning new things along the way, Apple is the right place for you. 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.
Description
The computer vision algorithm engineer will work in a dynamic team as part of the Video Computer Vision org. which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands.
Minimum Qualifications
Preferred Qualifications
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Description
The computer vision algorithm engineer will work in a dynamic team as part of the Video Computer Vision org. which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands.
Minimum Qualifications
- M.S. or PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning
- Rich experiences in machine learning and computer vision, covering one of the topics: Video Object Detection / Video Object Tracking / Depth Estimation / Neural Architecture Search
- Proven prototyping skills and proficient in coding (C, C++, Python)
- Excellent written and verbal communications skills, be comfortable presenting research to large audiences, and have the ability to work hands-on in multi-functional teams
Preferred Qualifications
- Publication record in relevant venues (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, SIGGRAPH).
- Industry experiences on machine vision applications or low level computer vision.
- Awards on public challenges in object detection / object tracking / depth estimation / NAS etc.
- Solid understanding of state-of-the-arts in Video Object Detection and Tracking, and familiar with the challenges of developing algorithms that run efficiently on resource constrained platforms. Team oriented, result oriented, and self motivated.
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