> Markdown version of [/jobs/ext/2709537-on-device-ml-infrastructure-engineer-orchestration-performance](https://www.wearedevelopers.com/jobs/ext/2709537-on-device-ml-infrastructure-engineer-orchestration-performance). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # On-device ML Infrastructure Engineer (Orchestration & Performance) - **Company:** Apple Inc. - **Location:** Cupertino, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** C++ (Programming Language), Software Debugging, Python (Programming Language), Machine Learning, Software Deployment, System Software, Pytorch, Machine Learning Operations - **Published:** September 4, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3377292050&tx=CT323THZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 3-5 years working on tooling built in Python 3 and C++/Swift * Familiarity with common ML model architectures, execution schemes, and operations. * Familiarity with PyTorch or related training frameworks., * Experience working on or adjacent to MLIR-based compilers. * Familiarity with deploying applications or tooling on Apple platforms. * Familiarity with programming paradigms for the GPU, CPU, and Neural Engine. * Familiarity with writing kernels for ML model execution. ## Description We're building an end-to-end developer experience for machine learning development that leverages Apple's vertical integration. This allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling, and analysis. This role focuses on the core runtime for execution across a wide variety of devices and use cases. We're seeking a highly motivated software engineer who is creative, talented, and passionate about machine learning, common compiler optimizations, and system software engineering in the fast-paced and dynamic field of machine learning. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Harnessing Apple Intelligence: Live Coding with Swift for iOS](https://www.wearedevelopers.com/videos/1515-harnessing-apple-intelligence-live-coding-with-swift-for-ios) - [Flexibility is Key: Unlocking the Advantages of Versatile Software Solutions for Strategic Innovatio](https://www.wearedevelopers.com/videos/1943-flexibility-is-key-unlocking-the-advantages-of-versatile-software-solutions-for-strategic-innovatio) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)