Machine Learning Research Intern - Machine Learning, Foundation Models

Summary

We are looking for a machine learning research intern with passion to spearhead new solutions to challenging problems in foundation model development with an emphasis on agentic capabilities and Reinforcement Learning. You will closely work with an applied ML team of around 35 engineers in Zurich, and get the chance to connect with other experts in the US.

Minimum Qualifications

PhD student in Machine Learning (Computer Science, Electrical & Computer Engineering, Statistics, Math, Natural Sciences, or other related fields) Strong background in Reinforcement Learning and Deep Learning, with hands-on experience training large models, in particular LLMs Experience with training methodologies including pre-training, fine-tuning, and alignment approaches Strong programming skills. Most relevant are Python, PyTorch, and JAX

Preferred Qualifications

Excellent problem solving, critical thinking, and interpersonal skills. Ability to define and drive ambitious goal-oriented research and development independently

Description

During your internship, you will do research and development in the context of Apple foundation models that power next-generation AI experiences across Apple’s platforms. You will learn what is relevant to transfer results from machine learning research to meaningful real-world performance at scale. Your work can contribute to Apple Intelligence and be shipped to billions of devices, worldwide.

Responsibilities

Research and develop novel approaches for enhancing foundation model capabilities Leverage state-of-the-art, large-scale RL methods to improve reasoning and planning, multi-turn conversations, and general agentic capabilities. Design and train agents with tool calling, planning, and API integration to reliably complete tasks. Investigate multimodal foundation models spanning text, vision, audio, and other modalities Collaborate on model alignment, safety, and responsible AI practices Contribute to the development of tools and frameworks for foundation model experimentation and evaluation