Machine Learning Engineer - Foundation Models

Summary

We are a group of engineers and researchers responsible for building foundation models at Apple. We build infrastructure, datasets, and models with fundamental general capabilities such as understanding and generating text, images, speech, video, and other modalities, and apply these models to Apple products. We are looking for passionate engineers and 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 will have the opportunity to work on innovative foundational research in machine learning. As a member of the team, you will be inspired by a wide range of challenging problems, collaborate with world-class machine learning engineers and researchers to shape next-generation Apple products, and publish your results in top-tier scientific venues.

Minimum Qualifications

PhD, or equivalent practical experience, in Computer Science or a related technical field. Demonstrated expertise in machine learning research or engineering. Hands-on experience working with deep learning toolkits such as JAX, TensorFlow, or PyTorch. Strong mathematical foundation in linear algebra and statistics. Fluent in both English and Mandarin Chinese.

Preferred Qualifications

Ability to formulate research problems, design experiments, and implement and communicate solutions. Ability to thrive in a diverse, collaborative environment. Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, ECCV, ACL, EMNLP). Experience in competitive programming (e.g., ICPC, NOI/IOI, or equivalent competitions).

Description

In this role, you will focus on pre-training, model architecture, scientific scaling of LLMs, and the associated infrastructure. Design efficient model architectures tailored to product and business requirements. Propose research plans to advance our understanding of machine learning and execute them through implementation and experimentation, in collaboration with colleagues. Optimize relevant workflows and/or infrastructure to improve training efficiency and scale. Collaborate with broader teams across Apple.