Internship - Pro Apps Machine Learning Ecosystem Engineer
Apple
Internship - Pro Apps Machine Learning Ecosystem Engineer
Job Description
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 application 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?
Locations
- London, England, United Kingdom W1S 1YZ
Salary
Estimated Salary Rangemedium confidence
800,000 - 1,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- high-performance data-parallel algorithmsintermediate
- machine learning inferenceintermediate
- machine learning trainingintermediate
- graphics software developmentintermediate
- frameworks developmentintermediate
- developer tools developmentintermediate
- GPU drivers optimizationintermediate
- display drivers optimizationintermediate
- CoreMLintermediate
- MPSGraphintermediate
- ML workloads integrationintermediate
- ML workloads optimizationintermediate
- ANE silicon optimizationintermediate
- GPU silicon optimizationintermediate
- ML APIs analysisintermediate
- optimizations recommendationintermediate
- algorithms recommendationintermediate
- techniques recommendationintermediate
- ML application developmentintermediate
- engineeringintermediate
- documentationintermediate
- trainingintermediate
- relationship managementintermediate
Required Qualifications
- Outstanding written and oral communication skills. (experience)
- Proven C++ programming and problem-solving skills. (experience)
- Good understanding of machine learning fundamentals. (experience)
- Machine learning development using one or more ML frameworks (PyTorch, TensorFlow, JAX). (experience)
Preferred Qualifications
- - (experience)
Responsibilities
- 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?
- 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
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Internship - Pro Apps Machine Learning Ecosystem Engineer
Apple
Internship - Pro Apps Machine Learning Ecosystem Engineer
Job Description
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 application 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?
Locations
- London, England, United Kingdom W1S 1YZ
Salary
Estimated Salary Rangemedium confidence
800,000 - 1,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- high-performance data-parallel algorithmsintermediate
- machine learning inferenceintermediate
- machine learning trainingintermediate
- graphics software developmentintermediate
- frameworks developmentintermediate
- developer tools developmentintermediate
- GPU drivers optimizationintermediate
- display drivers optimizationintermediate
- CoreMLintermediate
- MPSGraphintermediate
- ML workloads integrationintermediate
- ML workloads optimizationintermediate
- ANE silicon optimizationintermediate
- GPU silicon optimizationintermediate
- ML APIs analysisintermediate
- optimizations recommendationintermediate
- algorithms recommendationintermediate
- techniques recommendationintermediate
- ML application developmentintermediate
- engineeringintermediate
- documentationintermediate
- trainingintermediate
- relationship managementintermediate
Required Qualifications
- Outstanding written and oral communication skills. (experience)
- Proven C++ programming and problem-solving skills. (experience)
- Good understanding of machine learning fundamentals. (experience)
- Machine learning development using one or more ML frameworks (PyTorch, TensorFlow, JAX). (experience)
Preferred Qualifications
- - (experience)
Responsibilities
- 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?
- 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
Tags & Categories
Answer 10 quick questions to check your fit for Internship - Pro Apps Machine Learning Ecosystem Engineer @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs