Machine Learning Research Engineer, Pretraining Data — Foundation Models

Apple • Cupertino, CA

1d

About The Position

Apple’s Foundation Models team is developing large language models that power intelligent experiences across Apple products. The Pretraining Data team focuses on high-quality, diverse, and scalable training data to advance model capabilities in knowledge, reasoning, coding, and multilingual understanding.

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 approaches to improving training data quality, diversity, and efficiency, and build robust, scalable infrastructure for processing massive datasets. You will collaborate with model training teams to understand how data affects model capabilities, design controlled experiments, and improve pretraining strategies. The role combines cutting-edge machine learning research with large-scale systems engineering and contributes directly to the development of next-generation foundation models.

Requirements

• BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or 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.

Nice To Haves

• 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.

Responsibilities

• Develop algorithms and scalable systems for pretraining data curation, quality assessment, and optimization.
• Investigate approaches to improve training data quality, diversity, and efficiency.
• Build robust and scalable infrastructure for processing massive datasets.
• Collaborate with model training teams to understand the relationship between data and model capabilities.
• Design controlled experiments and continuously improve pretraining strategies.
• Translate research ideas into measurable improvements in foundation model capabilities.

What This Job Offers

• Job Type: Full-time