Reliability Engineering Intern

Beschreibung:

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.
Be a part of the Wearables Reliability team! We are responsible for ensuring and improving the durability and reliability of Apple's product. To achieve that, we partner with multiple engineering teams from the beginning (concept) to the end (field returns) of a product lifecycle, while exploring new technologies and understanding how customers will use our products. In this role, you will involve in daily work of product development in our factory and create program/scripts to automate the manual processes to improve the work efficiency and data accuracy. You will witness new Apple products being developed and how our data improve the product reliability. The position is factory lab based.
Currently pursuing a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. Proficiency in Python programming. Familiarity with Natural Language Processing (NLP) fundamentals and hands-on experience working with LLMs (via coursework, projects, or research). Experience working with APIs and basic data pipeline development Excellent written and interpersonal communication skills, proficiency in English
Self-motivated, capable of working independently and taking initiative Strong software development skills with experience in designing and building robust applications. Working knowledge of modern ML frameworks such as PyTorch, TensorFlow, or JAX. Familiarity with the latest research and tools in the LLM space Experience in designing and implementing information retrieval systems using embeddings (e.g., MiniLM), vector stores (e.g., Milvus, Qdrant), or similarity match & ranking techniques Problem-solving attitude and curiosity about how technologies work