AIML- Intern in ML Research, MLR

Summary

Posted: Oct 1, 2024
Weekly Hours: 40
Role Number:200570877

Play a part in building the next revolution of machine learning technology. We’re looking for passionate researchers in the final years of their post-graduate studies to work on ambitious curiosity driven research projects that will impact the future of Apple, and our products, through open research. In this role, you’ll have the opportunity to work on innovative foundational research in machine learning. As a member of the team, you will be inspired by a diversity of exciting problems, collaborate with world-class machine learning engineers and researchers to publish some of your results in high-quality scientific venues.

Description

You are towards your final years of a PhD programme in Machine Learning/Statistics/Computer Vision/NLP, and have already published some of your results in a main conference in the field. You will hone your research skills with us, as we go through the various collaborative phases of an ML research project: identify a promising research opportunity, survey SoTA methods and relevant literature, imagine and design novel methods, implement them as code prototypes, plan and complete experiments at multi-node, multi-GPU scales, write a paper and follow through with a submission.
Topics of interest include but are not limited to differentiable optimization (e.g. bi- and multi-level programming), generative modelling (diffusions, transport) and uncertainty quantification (conformal prediction, calibration). 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.

Minimum Qualifications

• Demonstrated expertise in machine learning research.
• Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, ECCV, ACL, EMNLP, etc).
• Hands-on experience working with deep learning toolkits such as JAX or PyTorch.
• 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.
• In the final years of a PhD programme.

Preferred Qualifications

• Ability to work in a diverse collaborative environment.
• In the final years of a PhD programme.
• Preferably, final year(s) of PhD in Computer Science, Computer Vision, Statistics, Applied Mathematics or NLP, all with an emphasis on ML methods. Students currently pursuing a MSc 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.