About the role

AI summarised

The Machine Learning Engineer will develop and optimize machine learning algorithms for video processing to enhance Apple's visual technologies. This role involves end-to-end ML workflow from data curation to feature productization, collaborating across teams to deliver high-impact features for consumer products. The position requires strong technical skills in ML frameworks and video processing, with opportunities to work on cutting-edge multimodal and generative models.

TechnologyOnsiteHardware

Key Responsibilities

Work closely with company-wide multiple teams and multiple projects
Engage in pre-training data curation and post-training data preparation at large scale
Help deliver new features for Apple products
Identify and develop machine learning solutions
Optimize and productize machine learning features
Build next-generation video processing features
Play a key role from data to feature development

Requirements

Master’s degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields
Knowledge of the principles, algorithms, and techniques used in machine learning and video processing
First-hand experience in machine learning and video processing
Strong experience in evaluating supervised, unsupervised, and deep learning models
Familiarity with multimodal models (e.g., image + text, video + audio) and related evaluation challenges
Proficiency in Python and libraries such as NumPy, pandas, scikit-learn, PyTorch, or TensorFlow
Strong communication skills
Documentation skills
PhD degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields (Preferred)
Knowledge of low-level vision algorithms such as spatial and temporal image/video processing (Preferred)
Publication record in top-tier conferences (e.g., CVPR, ICCV, SIGGRAPH, ECCV, NeurIPS, ICML, ICLR) (Preferred)
Experience evaluating generative models (e.g., text generation, image/video generation) (Preferred)
Excellent independent problem-solving skills (Preferred)
Hands-on experience working on MLLMs (Preferred)