Pro Apps ML Ecosystem Engineer
Role details
Job location
Tech stack
Job description
Apple's GPU, Graphics and Displays org provides the graphics software foundation across all of Apple's innovative products, including Mac, iPad, iPhone, and Apple TV. Our responsibilities encompass the frameworks, developer tools, and device drivers for the GPU and Display on Apple's hardware. MPSGraph provides a suite of high-performance data parallel algorithms for developers inside and outside of Apple to improve the ML inference potential and performance applications on for iOS, macOS and Apple TV., GGML's Metal Pro Apps and ML Ecosystem team is now seeking a world-class, 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. Are you ready to engage with developers to advance their success using Apple's tools and technologies related to ML? Responsibilities: Analyze developers products and report on their use of the ML API, recommending optimizations, algorithms, and techniques to improve their applications. Contribute platform enablement and optimisations to key open source projects. Host developers during in-person engineering sessions. You will be a key source for all aspects of ML application development, including engineering, documentation, training, and relationship management.","responsibilities":"Support adoption of GPU-accelerated training by 3rd party Apps and key middleware. 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 and open source communities, managing the relationships, and driving the engineering work
Requirements
GPU compute programming models & optimization techniques.
Machine learning development using one or more ML frameworks (PyTorch, TensorFlow. or JAX). Experience with system level programming and computer architecture., GPU compute framework development, maintenance, and optimization. Proven programming and problem-solving skills. Good understanding of machine learning fundamentals. Exemplary written and oral communication skills.
