Intern

Summary

Application Deadline: Wednesday 31st December Do you want to play a part in building the next revolution in machine learning technology? 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.

Minimum Qualifications

Students currently pursuing a MSc or a PhD in Computer Science or Mathematics, with a specialisation in ML, and very strong coding skills as demonstrated by participations in open source projects may also apply. Proven expertise in machine learning research. Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, CVPR, ICCV, ECCV, ACL, EMNLP, etc). Hands-on experience working with deep learning toolkits such as JAX or PyTorch.

Preferred Qualifications

Ability to work in a diverse collaborative environment. Strong mathematical skills in linear algebra, probability, optimization and statistics. Strong coding skills. Ability to formulate a research problem, design, experiment, implement and communicate solutions

Description

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) optimization (e.g. bi- and multi-level programming, scaling laws for NN training, parameterization), uncertainty quantification, data-centric ML (curriculum learning, data reweighting). You’ll also have the opportunity to collaborate further with MLR colleagues outside of Paris on the project. Ultimately, you will work towards publishing new findings arising from the project, either or both as open source code and publications.