AIML - Machine Learning Researcher, MLR

Play a part in building the next revolution in machine learning technology. We’re looking for passionate researchers to work on ambitious, curiosity-driven, long-term research projects. 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 challenging problems, collaborate with world-class machine learning engineers and researchers to impact the future of Apple products, and publish some of your results in high-quality scientific venues.

Description
You have a strong research background in machine learning or related fields and regularly publish your work in leading conferences and journals. You are committed to producing high-quality, reproducible research.
In this role, you will have the opportunity to define and pursue a research agenda focused on fundamental problems in machine learning, including efficient training algorithms and models with fast inference, especially with multi-modal, multi-task, and reasoning capabilities. You will advance our understanding of these areas through rigorous experimentation and close collaboration with colleagues. You will also develop high-quality code, provide technical mentorship, and prepare publications and presentations for top-tier venues. Additionally, you will have opportunities to collaborate with broader teams across Apple.

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.)
Strong hands-on experience working with Python and deep learning toolkits such as PyTorch.
Strong mathematical skills in linear algebra and statistics
PhD or equivalent practical experience in Computer Science or a related technical field

Preferred qualifications
Ability to formulate a research problem and design, experiment, implement, and communicate solutions.
Ability to work in a diverse, collaborative environment
High-quality open-source contributions to related projects
Strong presentation skills for internal and external communications