Machine Learning Engineer Intern - Shanghai

Apple is a place where extraordinary people gather to do their best work. Just be ready to dream big. The people here at Apple don’t just build products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that encourages the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Imaging what you could do here. At Apple, creative ideas have a way of becoming wonderful products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish.

Description

The Hardware team features a collaborative environment with creative, smart talents, world-class products and cutting-edge technologies. The team provides opportunities for individuals to contribute across a wide spectrum of disciplines. Best-in-class engineering excellence and thoroughness are expected and encouraged. Innovation is highly supported and valued. Pushing the envelope to design and ship innovative products with best-in-class technologies and user experiences are the main goals of the Hardware Test Engineering (HWTE) team.

This individual will play a key role in Machine Learning tools design and development to enable advanced anomaly reasoning and core manufacturing models. You will work closely with cross-functional groups to ensure the success of current and future Apple products.

Minimum Qualifications

Currently pursuing a BS, MS, or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Vision, or a related field.
Available to join internship for 3-6 months full-time.
Solid foundation in machine learning algorithm development and data architecture, with deep knowledge of traditional ML algorithms and deep learning frameworks (PyTorch, TensorFlow).
Strong programming skills in Python and ML libraries.
Self-motivated, responsible, with excellent written and verbal interpersonal skills.

Preferred Qualifications

Experience with Large Language Models (LLMs), prompt engineering, and multimodal data integration.
Familiarity with computer vision and image processing for anomaly detection.
Experience in data analysis, pipeline optimization, and system latency reduction.
Familiarity with version control (Git), PRs, and collaborative software development.