AIML - Machine Learning Engineer, Foundation Models

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. Minimum Qualifications MS or PhD in Computer Science, Machine Learning, or related technical field Expert-level programming skills in Python Proficiency in machine learning frameworks such as Jax, PyTorch, TensorFlow Strong background in: Distributed training, Model optimization, and Machine learning infrastructure Experience with large-scale model training and deployment Familiarity with: Kubernetes, Docker, Cloud platforms (AWS, GCP, Azure) Distributed computing frameworks Preferred Qualifications Experience with foundation models and large language models Background in multi-modal AI systems Demonstrated ability to transform research prototypes into production systems Published research or significant contributions to open-source ML projects Understanding of on-device machine learning techniques