Computer Vision/Machine Learning Engineer (Low level CV)

Apple

  • Beijing
  • Permanent
  • Full-time
  • 30 days ago
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.DescriptionThe computer vision algorithm engineer will work in a dynamic team as part of the Video Engineering 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. Keywords: Machine learning based ISP; Low level object detection and segmentation; Multiple sensor fusionMinimum 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 video machine learning covering one of the topics: Low level object detection and segmentation / ML-based ISP / Multi-sensory fusion / Multi-modal fusion in ISP
  • 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 with multi-modal foundation model and frameworks
  • Knowledge and understanding of generative AI, multi-modal large language model, video caption
  • Solid understanding of state-of-the-arts in Video Understanding and familiar with the challenges of developing algorithms that run efficiently on resource constrained platforms

Apple