Internship Machine Learning Research

Application Deadline: 31st December 2026nnWere 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 this internship youll dive into innovative foundational research in machine learning. Youll 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.

Students currently pursuing a MSc or a PhD in Computer Science Machine Learning or equivalentnPublication record in relevant conferences (e.g. NeurIPS ICML ICLR AISTATS CVPR ACL EMNLP etc).nHands-on experience working with deep learning toolkits such as JAX PyTorch or skills needed to operate within and receive feedback from a large group of researchers.

Strong mathematical skills in linear algebra probability optimization and to formulate a research problem paired with strong prototyping/coding skillsnAbility to design experimental plans and communicate progress.