About The Position
We are seeking passionate researchers in the final years of their post-graduate studies to undertake high-reaching, curiosity-driven projects that will shape the future of Apple and its products through open research. This internship offers an opportunity to dive into innovative foundational research in machine learning, tackle a variety of impactful problems, and collaborate with leading machine learning engineers and researchers. Interns will have the chance to share their work through publications in top-tier scientific venues. The role involves sharpening research skills through collaborative stages of an ML research project, including identifying research opportunities, reviewing state-of-the-art methods and literature, developing novel approaches, implementing code prototypes, planning and executing large-scale experiments on multi-node, multi-GPU systems, writing and submitting papers. Topics of interest encompass generative modeling (diffusions, discrete diffusions, flows, transport), efficient inference (architectures, context management, kv compression), optimization (e.g., scaling laws for LLM training, parameterization), uncertainty quantification, and data-centric ML (curriculum learning, data reweighting). Collaboration extends to MLR colleagues in other European locations and the US, with the goal of publishing new findings as open-source code and/or publications.
Requirements
- 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.
- 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.
Nice To Haves
- Final years of a PhD programme in Machine Learning, Statistics, Computer Vision or NLP.
Responsibilities
- Identify promising research opportunities.
- Review state-of-the-art methods and relevant literature.
- Craft novel approaches.
- Implement approaches as code prototypes.
- Plan and run large-scale experiments across multi-node, multi-GPU systems.
- Write and submit research papers.
- Collaborate with MLR colleagues in various locations.
- Publish new findings as open-source code and/or publications.
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What This Job Offers
Career Level
Intern
