Internship Computer Vision & Machine Learning Research - LLM Efficiency

We're seeking research interns to create breakthrough innovations in machine learning. We are particularly interested in efficiency of frontier models, including LLMs and diffusion models. You will work in 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 are in your final years of a PhD programme in Machine Learning and have already published some of your work at top-tier venues in the field. During your time with us, you will continue sharpening your research skills as we go through the various collaborative stages of an ML research project: 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, and seeing it through to submission.

Topics of interest include but are not limited to frontier models, efficiency of LLM inference and training, on-device models, speculative decoding, contextual sparsity, quantization and compression. We are a team of best-in-the-world research scientists and engineers with deep experience in computer vision, machine learning, robotics, computer graphics, and related areas. We work on exciting new technologies that bring joy to millions of people.

In our daily work, the team stays innovative, productive, and fun by sharing some key values:

- Passion for the mission: We're here to make something extraordinary. We seek whatever work is right and strive for the best possible results.
- Modesty: The right answer is more significant than being right. We search for solutions as a team and value clear-eyed feedback.
- Lean habits: You can't grow without limits. Time constraints and big goals encourage us to sharpen our focus and learn to make phenomenal decisions.

Minimum Qualifications

You are working towards a doctoral degree in computer science, engineering, data science, applied mathematics, or equivalent.
6 months dedication is preferred, and starting no later than March 2026
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.

Preferred Qualifications

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. You deliver clean, well-tested code.