Machine Learning Engineer, Apple Foundation Models

What you'll do

• Design and iterate on end-to-end post-training strategies including Reinforcement Learning to unlock specific model behaviors.
• Pioneer novel algorithms for preference optimization, model steering, and safety.
• Research methods for high-quality human and synthetic data generation, automated filtering, and curriculum learning.
• Develop robust evaluation methodologies to measure model helpfulness, factuality, and real-world utility beyond static benchmarks.
• Improve model performance on complex reasoning tasks such as math, coding, and logic.
• Translate user requirements from product teams into specific technical capabilities for the models.
• Inform architectural choices by collaborating with pre-training teams during the development of foundation models.

What we're looking for

• Demonstrated expertise in deep learning with a focus on LLMs, post-training, or reinforcement learning.
• Proficiency in Python and a major deep learning framework such as JAX or PyTorch.
• Master's or PhD degree in Computer Science, Machine Learning, or a related technical field (or equivalent practical experience).
• Experience training state-of-the-art large models at scale with familiarity in distributed training challenges (preferred).
• Experience improving model performance on complex reasoning tasks such as math, coding, and logic (preferred).
• Experience with various transformer architectures and their transformations (preferred).
• Strong communication skills and a passion for working cross-functionally across Research and Product teams (preferred).