AIML - Machine Learning Researcher, MLR

Descripción del puesto:

Play a part in building the next revolution of machine learning technology. We're looking for a passionate researchers to work on ambitious curiosity-driven long-term research projects that will impact the future of Apple, and our products. In this role, you'll have the opportunity to work on innovative foundational research in machine learning focusing on LLMs and generative models. 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, and publish your results in high-quality scientific venues.

Description

You have a strong research background in machine learning or related fields, and regularly publish your results in the main relevant conferences, and make sure that your research results are of high quality and reproducible. You will define your research plan to advance our understanding of machine learning and execute it through implementation and experimentation, in collaboration with your colleagues. You will provide technical mentorship and guidance, and prepare technical reports for publication and conference talks. You will have the opportunity to collaborate with broader teams across Apple.

Minimum Qualifications

* PhD, or equivalent practical experience, in Computer Science, or related technical field
* Demonstrated 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, PyTorch or MLX

Preferred Qualifications

* Strong mathematical skills in differential calculus, probability, statistics.
* Strong coding skills, as exemplified by e.g. OSS contributions, and ability to maintain a coherent and evolving codebase.
* Ability to formulate a research problem, design, experiment, implement and communicate solutions.
* Ability to work as a team player in a diverse collaborative environment.
* You have proposed through previous publications impactful methods in areas of interest to the group, such as generative modeling (flow matching, diffusions, etc.) or LLM/VLM training/fine-tuning/inference