Machine Learning Research Engineer - Pretraining Data, Foundation Models

Summary

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.

Minimum Qualifications

BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or 5+ years of equivalent practical experience. Strong programming skills in Python and proficiency in developing production-quality software. Experience with large-scale data processing, distributed computing, or machine learning systems. Solid understanding of machine learning fundamentals and modern deep learning architectures. Strong analytical and problem-solving skills, with the ability to independently investigate complex technical challenges.

Preferred Qualifications

Experience with LLM pretraining, training data research, or foundation model development. Experience designing and implementing scalable data processing pipelines using Spark, Ray, or similar distributed computing frameworks. Hands-on experience with PyTorch, model training, and experimental evaluation. Familiarity with data filtering, deduplication, data mixture optimization, and learned data quality signals. Understanding of scaling laws, training dynamics, and the relationship between data and model performance. Experience with synthetic data generation, data selection, or model-based data evaluation. Demonstrated ability to translate research ideas into scalable production systems. Publications or meaningful contributions to machine learning research or open-source projects.

Description

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.

Responsibilities

Research, design, and implement novel algorithms for large-scale pretraining data curation, including quality filtering, deduplication, data selection, and mixture optimization. Build and optimize scalable data processing systems capable of handling trillions of tokens across diverse data sources. Develop machine learning-based approaches for data quality assessment, synthetic data generation, and data efficiency optimization. Collaborate with foundation model researchers to design and execute training experiments, analyze model performance, and identify opportunities for improving model capabilities. Investigate the relationship between training data characteristics, model scaling, training dynamics, and downstream evaluation results. Translate research prototypes into efficient, reliable, and production-ready data systems. Stay current with advances in foundation model research and apply emerging techniques to practical data challenges.