Computer Vision/Machine Learning Engineer (Photography Intelligence)

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 photography intelligence algorithm engineer will work in China Vision Lab as part of the Video Engineering org which develops on-device computer vision and machine perception technologies across Apple’s products. The role is responsible for designing and implementing machine learning systems that understand the scene as well as user intent before and during capturing photos or videos. It bridges visual perception, semantic understanding, and decision intelligence, enabling smart photography and videography experience. 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 video machine learning covering one of the topics: Computational Photography / Visual Reasoning Algorithms / VLM or MLLM / Camera Control
• 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

• Publications in top-tier conferences (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, SIGGRAPH)
• Solid understanding and industry experiences on computational photography, visual perception or reasoning algorithms, MLLM, camera control pipeline, etc
• Familiar with the challenges of developing algorithms that run efficiently on resource constrained platforms
• Team oriented, result oriented, and self motivated