Job Details

Role Number: 200649940-3543

Summary

The codec deep video processing team develops machine learning algorithms to power Apple technologies with the best user visual experience. In this role, you will work closely with company-wide multiple teams and in multiple projects, from data curation to model development, in a large-scale, to help deliver new features for Apple products and bring high impact to millions of users.

Description

Join us as a Machine Learning Engineer and build the next-generation video processing features. You will play the key role from data to feature development. In this role, you will identify and develop machine leaning solutions and work closely with multiple teams to optimize and productize those features.

Minimum Qualifications

• BS and a minimum of 3 years relevant industry experience.

• Experience evaluating supervised, unsupervised, and deep learning models.

• Proficiency in Python and libraries such as NumPy, pandas, scikit-learn, PyTorch, or TensorFlow.

• Knowledge of the principles, algorithms, and techniques used in machine learning and video processing with first-hand experiences.

Preferred Qualifications

• PhD or Master degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields.

• Knowledge of low-level vision algorithms including spatial and temporal image/video processing.

• Experience working with multimodal and multimodal large language models (MLLMs) (e.g., image-text, video-audio systems).

• Experience evaluating generative and multimodal models, including benchmarking and quality assessment.

• Publication record in top-tier conferences (e.g., CVPR, ICCV, SIGGRAPH, ECCV, NeurIPS, ICML, ICLR)

• Strong communication skills and documentation skills.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .