Internship - Machine Learning Research

Application Deadline: 31st December 2026

We're looking for passionate researchers in the final years of their post-graduate studies to solve high-reaching, curiosity-driven projects that build the future of Apple and our products through open research. In this internship, you’ll dive into innovative foundational research in machine learning. You'll solve a variety of impactful problems, collaborating with leading machine learning engineers and researchers, with the chance to share your work through publications in top-tier scientific venues.

You are in your final years of a PhD programme in Machine Learning, Statistics, Computer Vision or NLP, and have already published some of your work at major conferences 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 SoTA 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 generative modelling (diffusions, discrete diffusions, flows, transport), efficient inference (architectures, context management, kv compression), optimization (e.g. scaling laws for LLM training, parameterization), uncertainty quantification, data-centric ML (curriculum learning, data reweighting). You will also have the opportunity to collaborate further with MLR colleagues outside of Paris, in other Europe locations and in the US. Ultimately, you will work towards publishing new findings arising from the project, either or both as open source code and publications.

Minimum Qualifications

• Students currently pursuing a MSc or a PhD in Computer Science, Machine Learning or equivalent

• Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, CVPR, ACL, EMNLP, etc).

• Hands-on experience working with deep learning toolkits such as JAX, PyTorch or MLX.

• Teamwork skills needed to operate within and receive feedback from a large group of researchers.

Preferred Qualifications

• Strong mathematical skills in linear algebra, probability, optimization and statistics.

• Ability to formulate a research problem, paired with strong prototyping/coding skills

• Ability to design experimental plans and communicate progress.