AIML - Machine Learning Engineer, Foundation Models

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.

Description

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