About the role

AI summarised

Machine Learning Engineer at Apple's codec deep video processing team, developing ML algorithms to enhance user visual experience across Apple products. The role involves working on large-scale data curation and feature development, collaborating with multiple teams to deliver new video processing features.

TechnologyFull-timeHardware

Key Responsibilities

Develop machine learning algorithms to power Apple technologies with the best user visual experience
Work closely with company-wide multiple teams and in multiple projects
Handle pre-training data curation to post-training data preparation in a large-scale
Help deliver new features for Apple products and bring high impact to millions of users
Build the next-generation video processing features
Play the key role from data to feature development
Identify and develop machine learning solutions
Work closely with multiple teams to optimize and productize those features

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 with first-hand experiences
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 and 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)