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# On-Device ML Compiler Engineer, Model Compilation, Graphics, Games and Machine Learning

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Experienced
- **Salary:** $147,400.0 - $272,100.0
- **Contract:** Permanent contract
- **Skills:** Apple Watch, Macintosh Computers, C++ (Programming Language), Software Debugging, Machine Learning, System Software, Pytorch, Machine Learning Operations
- **Published:** May 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e6ace1285e943598

## About the Role

Do you have experience in Model training?, We're seeking a highly motivated software engineer who is creative, versatile, and passionate about machine learning, common compiler optimizations, and system software engineering in the fast-paced and dynamic field of machine learning. We have an MLIR-based compiler stack, and use it to target the neural engine, GPU, and CPU in order to harness the full capabilities of the system for ML workflows and execution., Familiarity with Swift.

Familiarity with programming paradigms for the GPU, CPU, and Neural Engine.

Familiarity with writing kernels for ML model execution.

Minimum Qualifications

3-5 years working on MLIR-based compilers.

Familiarity with common ML model architectures, execution schemes, and operations.

Familiarity with C++

Familiarity with PyTorch or related training frameworks

## Description

We're building an end-to-end developer experience for machine learning development that employs 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., ","responsibilities":"Inspire changes in our MLIR-based compiler in order to target improved runtime performance by demonstrating the capabilities of the hardware.

Propose upstream changes in MLIR to better support new features and workflows in the hardware that lead to more optimal execution performance across all types of devices and device clusters.

Own core pieces of the compiler stack enabling heterogeneous compute across Apple devices. We target execution of ML models across the Apple ecosystem from resource-constrained devices like Apple Watch, to the high-end Macs with Ultra SoCs.

Work closely with hardware, software, and performance teams across the company to accelerate and optimize execution by taking advantage of the latest features in the hardware, OS, and drivers.


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