Engineering Intern - AI/ML & Automation for Hardware

Apple is hiring an Engineering Intern - AI/ML & Automation for Hardware in Shanghai, China. When: Summer 2027. Posted October 27, 2026. Checked against the employer's posting on November 12, 2026.

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Description Our Product Safety Engineering Team is looking for a talented and enthusiastic Engineering Intern (AI/ML & Automation) to help develop cutting-edge AI-driven tools, data analysis platforms, and lab automation software. In this role, you will work at the intersection of AI/ML, full-stack software development, and hardware safety validation—building intelligent solutions that accelerate test data processing, safety compliance analysis, risk assessment, and test automation workflows.

Minimum qualifications • Currently pursuing a Master’s degree or PhD in Computer Science, Software Engineering, Electrical/Electronic Engineering, Mechanical/Automation Engineering, or a related technical field. • Able to commit to a minimum 6 months full-time internship in Shanghai. • Strong programming proficiency in Python etc. • Solid understanding of web application development (HTTP, RESTful APIs, modern frontend/backend architecture). • Familiarity with database fundamentals (SQL queries, data schema design). • Strong analytical, problem-solving, and debugging skills. • Good written and spoken communication skills in both English and Mandarin Chinese.

Preferred qualifications • Experience with AI/ML frameworks and libraries (e.g., PyTorch, Hugging Face, LangChain, LlamaIndex, or LLM API integrations). • Hands-on web development experience using FastAPI / Flask and React / Next.js. • Knowledge of vector databases (e.g., Qdrant, Pinecone, FAISS) and embedding/retrieval (RAG) pipelines. • Experience or interest in hardware test automation, lab instrumentation, sensor data acquisition, or safety/reliability testing workflows. • Familiarity with containerization (Docker), Git version control, and Linux environments. • Proactive, curious, self-driven, and comfortable collaborating in a fast-paced engineering team.