AIML - Machine Learning Researcher, Post-Training for Foundation Models

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About this role

AIML - Machine Learning Researcher, Post-Training for Foundation Models Cupertino, United States of America We are a group of engineers and researchers responsible for building foundation models at Apple. Within this group, the Post-Training work streams focus on transforming powerful pre-trained checkpoints into helpful, high-quality models that power billions of Apple products. We are looking for researchers who are passionate about foundation model post-training, including Supervised Fine-Tuning (SFT), Reinforcement Learning, with experiences in core capabilities such as instruction following, tool use, deep thinking and reasoning. We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team. Recipe Development: Design and iterate on end-to-end post-training recipes, combining SFT, Reinforcement Learning and reasoning regimes to achieve specific model behaviors and capabilities. Algorithm Research: Develop and implement novel algorithms for preference optimization, model steering, and safety. Data Strategy: Research methods for high-quality human and synthetic data generation, automated data filtering,

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