Machine Learning Research Engineer, Pretraining Data — Foundation Models

Apple is revolutionizing artificial intelligence by developing sophisticated foundation models that power intelligent features across our product ecosystem. We're seeking skilled Machine Learning Engineers to transform cutting-edge research into scalable, production-ready AI solutions. We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products. Apple's Foundation Models team is building the next generation of large language models that power intelligent experiences across Apple products. High-quality, diverse, and scalable training data is fundamental to advancing model capabilities in knowledge, reasoning, coding, and multilingual understanding. We are seeking talented and motivated Research Engineers to advance the state of the art in pretraining data research and engineering. In this role, you will develop novel algorithms and scalable systems for data curation, quality assessment, and optimization, working closely with model training researchers to translate innovative ideas into measurable improvements in foundation model capabilities. This position offers a unique opportunity to work at the intersection of cutting-edge machine learning research and large-scale systems engineering, with direct impact on the development of next-generation foundation models.

As a Research Engineer on the Pretraining Data team, you will own technically challenging projects spanning research, algorithm development, experimentation, and production engineering. You will investigate new approaches to improving training data quality, diversity, and efficiency, while building robust and scalable infrastructure to support the processing of massive datasets. You will collaborate closely with model training teams to understand the relationship between data and model capabilities, design controlled experiments, and continuously improve pretraining strategies.