About the Role
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.
Our team builds frontier foundation models that power intelligent experiences at Apple. Our work spans 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.
Responsibilities
- Design and implement scalable, reliable and high-performance machine learning infrastructure for foundation models across text, image, speech, and multi-modal domains
- Collaborate with other teams to productionize state-of-the-art AI algorithms
- Optimize models for performance, efficiency, and on-device intelligence
- Implement machine learning systems with stringent privacy and security requirements
- May also be required to manage a small team of engineers.
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), and 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