Internship - Machine Learning Research ( RNNs | Large Language Models | Time-series analysis )

Application Deadline: Sunday 9th November

Play a part in the revolution of machine learning research. Explore innovative methods, challenge existing metrics and protocols, develop new informative theories that reshape our understanding of machine learning. The Machine Learning Research team led by Samy Bengio is looking for hard-working researchers to investigate the next generation of ML techniques, exploring innovative methods for training Recurrent Neural Networks at scale, challenging existing approaches to sequence-to-sequence modeling, and developing new techniques that advance language modeling and time-series analysis.

As a member of the team, you will tackle some of the most challenging scientific questions, collaborate with world-class machine learning researchers, drive innovative research agendas and publish ground-breaking research at international conferences.

Description

Are you passionate about advancing the state-of-the-art in machine learning and driving meaningful innovation? As an intern in the Machine Learning Research team, you will receive technical mentorship and guidance from a dedicated senior researcher and collaborate with a dynamic, multidisciplinary and international team of researchers and engineers. Your research will focus on developing and scaling recurrent architectures for sequence modeling, investigating novel training methodologies that overcome traditional computational limitations of RNNs, and apply these advances to real-world problems in language modeling and time-series analysis. The specific research direction will be decided together with your mentor, and your work will be geared towards a publication at a top machine learning venue by the end of the internship. You will be responsible for delivering high-quality ML research aligned with Apple's core value, ensuring the highest standards of quality, scientific rigour, innovation, and respect for user privacy.

This internship offers a unique opportunity to work at the intersection of Machine Learning, Numerical Analysis, and High-Performance Computing, granting exposure to truly interdisciplinary research while contributing to reshaping how we approach sequence modeling at scale.

Preferred Qualifications

Strong software engineering skills, with emphasis on writing efficient and maintainable research code

Experience working with language modelling, time-series analysis, and/or RNNs

Familiarity with concepts of parallelization and High-Performance Computing

Strong mathematical skills in linear algebra / numerical analysis / control theory

Ability to work in a collaborative environment

Minimum Qualifications

Pursuing a PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, or a related technical field.

Proven expertise in machine learning supported by a strong publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, CVPR, ICCV, ECCV, ICASSP, InterSpeech, AAAI, ACL, FAccT).

Hands-on experience working with Python and deep learning toolkits such as PyTorch and Jax.

Ability to formulate a research problem, design, experiment and implement solutions.

Familiarity with modern sequence modelling architecture (Transformer, Mamba)

At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced, and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. We will work with applicants to make any reasonable accommodations.