Internship - Pro Apps Machine Learning Ecosystem Engineer

Internship - Pro Apps Machine Learning Ecosystem Engineer

London, England, United Kingdom

Software and Services

Application Deadline: Sunday 16th November

Apple's Metal Pro Apps Ecosystem team, within the Graphics, Games, and Machine Learning (GGML) organization, develops a suite of high-performance data-parallel algorithms used by developers across Apple platforms—including iOS, macOS, and Apple TV. This organization builds the graphics software foundation for all of Apple's cutting-edge products. Our work spans frameworks, developer tools, and GPU and display drivers optimized for Apple hardware. Through technologies like CoreML and MPSGraph, we empower developers to improve the performance and potential of machine learning inference and training in their applications.

Description

GGML's Metal Pro Apps Ecosystem team is now seeking an extraordinary, driven, and dedicated engineer to work alongside developers to integrate and optimize ML workloads for Apple's ANE and GPU silicon into their macOS and iOS applications. As an intern you will analyze developers products and report on their use of the ML APIs; recommending optimizations, algorithms, and techniques to improve their applications. You will also be a key source for all aspects of ML application development, including engineering, documentation, training, and relationship management. Are you ready to engage with developers to advance their success using Apple's tools and technologies related to ML?

Responsibilities

• Support adoption of GPU-accelerated inference and training by 3rd party Apps.
• Performing in-depth analysis of an application's full stack to drive the best possible performance across hardware families.
• Representing Apple to these 3rd party clients, maintaining the relationships, and driving the engineering work.

Minimum Qualifications

• Outstanding written and oral communication skills.
• Proven C++ programming and problem-solving skills.
• Good understanding of machine learning fundamentals.
• Machine learning development using one or more ML frameworks (PyTorch, TensorFlow, JAX).

Preferred Qualifications