Computer Vision Algorithm (Video Understanding) - Machine Learning Engineer Intern (Start ASAP)

Summary

If you are the kind of person who is passionate about pursuing excellence, embracing challenges, enjoying work with others, and 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 intern 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

• During the internship, you must be enrolled in an M.S. or PhD program 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: Video Understanding / Video Foundation Model / Multi-modal LLM
• Proven prototyping skills and proficiency in coding (C, C++, Python)
• Excellent written and verbal communication skills, 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 models and frameworks.
• Knowledge and understanding of generative AI, multi-modal large language model, and video caption.
• Solid understanding of state-of-the-art Video Understanding and familiarity with the challenges of developing algorithms that run efficiently on resource-constrained platforms.
• Team-oriented, result-oriented, and self-motivated.