SWE Intern - Machine Learning Engineer, Language Models

Beschreibung:

We are a group of engineers and researchers responsible for building large language models (LLMs) and generative models at Apple. We build infrastructure, datasets, and models with fundamental general capabilities such as understanding and generation of text, images, speech, videos, and other modalities and apply these models to Apple products. We are looking for passionate interns who are eager to develop algorithms, techniques, and systems that push the frontier of deep learning and delight millions of users with Apple products powered by generative models.
We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. As an intern, you will work with a close-knit and fast-growing team of world-class engineers and scientists to tackle some of the most challenging problems in LLMs and deep learning. In this internship role, you will focus on areas such as pretraining, large language model (LLM) architecture, and scientific scaling of LLMs. Experiences on full-stack LLM optimization such as reinforcement learning, data research and kernel optimization (e.g. pallas and triton) will be a plus. Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas improve the experience of billions of users.
Currently pursuing a Bachelor's degree or above (Master's or PhD) in Computer Science, Artificial Intelligence, or a related technical field Solid understanding of deep learning concepts and strong interest in applying large language models to real-world products Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow Ability to work in a collaborative environment
Publication record in relevant top-tier conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP) Experience and proven track record in computer science competitions (e.g., ACM-ICPC, NOI/IOI, or Kaggle) Experience in coding and training large language models Knowledge in reinforcement learning, on-policy distillation Familiarity with LLM context lengthening techniques