Internship Computer Vision & Machine Learning Research - LLM Efficiency

Apple

4d

About The Position

We are seeking research interns to create breakthrough innovations in machine learning, with a particular interest in the efficiency of frontier models, including LLMs and diffusion models. You will work within an organization of world-class machine learning researchers and engineers. Our work powers cutting-edge technologies across the Apple ecosystem and is published in the most selective scientific journals and conferences. You will continue sharpening your research skills through collaborative stages of an ML research project: identifying research opportunities, reviewing literature, crafting novel approaches, implementing prototypes, running large-scale experiments, writing and submitting papers. Topics of interest include frontier models, efficiency of LLM inference and training, on-device models, speculative decoding, contextual sparsity, quantization, and compression. We are a team of research scientists and engineers with deep experience in computer vision, machine learning, robotics, computer graphics, and related areas, working on exciting new technologies.

Requirements

• You are in your final years of a PhD programme in Machine Learning
• Already published some of your work at top-tier venues in the field
• Working towards a doctoral degree in computer science, engineering, data science, applied mathematics, or equivalent
• Proven research expertise in machine learning
• Publication record in relevant conferences (e.g., NeurIPS, ICLR, ICML, COLM, etc)
• Solid software engineering skills in complex, multi-language systems
• Fluency in Python
• Expertise in ML algorithms and top practices for working with deep learning systems
• Proficiency with ML modeling frameworks (PyTorch, Tensorflow, etc.)
• Strong overall software development approach
• Deliver clean, well-tested code

Nice To Haves

• 6 months dedication is preferred
• Starting no later than March 2026

Responsibilities

• Identifying a promising research opportunity
• Reviewing state-of-the-art methods and relevant literature
• Crafting novel approaches
• Implementing them as code prototypes
• Planning and running large-scale experiments across multi-node, multi-GPU systems
• Writing a paper
• Seeing it through to submission

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree