Internship - Machine Learning and AI - Foundation Model Infrastructure
il y a 1 semaine
Are you interested in solving challenging technical problems and contributing to systems that support large-scale machine learning models? The ML Infrastructure team, part of the Machine Learning Platform Technologies organization at Apple, builds frameworks, services, and tools that power Apple's foundation models on servers. Our infrastructure supports a wide range of Apple products and services, including Search, Music, TV, App Store, Messages, Photos, Safari, Siri, and more. It handles millions of requests each day with a focus on efficiency, scalability, and reliability. You will have the opportunity to work on technologies that make large language, vision, and speech models more efficient and deployable at scale. You'll collaborate with experienced engineers, learn about modern ML infrastructure, and help improve the systems that bring intelligent features to Apple products used by people around the world.
Description
As an intern on this team, you will:
- Collaborate with engineers to analyze and optimize inference performance for language, vision, and multimodal models.
- Develop tools and utilities that help understand bottlenecks across hardware platforms and workloads.
- Contribute to the design and evaluation of new techniques for efficient model serving and deployment.
- Learn about distributed systems, GPU computing, and large-scale ML workflows in a production environment.
- Work in an environment that values creativity, curiosity, and collaboration. This internship will provide hands-on experience with the infrastructure behind Apple's AI technologies and offer insight into how research ideas are transformed into scalable, reliable systems.
Minimum Qualifications
Currently pursuing a Master's degree in Computer Science, Machine Learning, or a related field.
Solid understanding of ML fundamentals, data structures, and algorithms.
Proficiency in Python and good coding practices.
Strong collaboration, curiosity, and problem-solving skills.
Preferred Qualifications
Experience with PyTorch or TensorFlow.
Familiarity with Transformer models or large-scale ML architectures.
Exposure to GPU programming, Docker, Kubernetes, or cloud environments.
Interest in distributed systems, inference optimization, or ML deployment.
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