全职 Machine Learning Engineer 工作, 薪水, Apple 公司招聘中 - Ricebowl

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 pre-training data curation to post-training data preparation in a large-scale, to help deliver new features for Apple products and bring high impact to millions of users.

Description
Join us as an ML 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.","responsibilities":"Design and develop data curation pipeline for pre-training and post-training
Design and implement deep learning algorithms for video related tasks
Design test suite and evaluation pipeline for validation and testing
Optimize models and algorithms for performance, including latency, memory, and computational efficiency
Integrate solutions into end-to-end video processing pipelines

Preferred Qualifications
PhD degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields
Knowledge of low-level vision algorithms such as spatial and temporal image/video processing
Publication record in top-tier conferences (e.g., CVPR, ICCV, SIGGRAPH, ECCV, NeurIPS, ICML, ICLR)
Experience evaluating generative models (e.g., text generation, image/video generation)
Excellent independent problem-solving skills
Hands-on experience working on MLLMs

Minimum Qualifications
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