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# On-device ML Infrastructure Engineer (ML Compiler)

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Experienced
- **Salary:** $181,100.0 - $318,400.0
- **Contract:** Permanent contract
- **Skills:** Apple Watch, Macintosh Computers, C++ (Programming Language), Nvidia CUDA, Machine Learning, Tensorflow, System Software, Pytorch, Machine Learning Operations
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3f41d984a9bbf94a

## About the Role

Knowledge of other ML frameworks and ML pipelines

Familiarity with Swift.

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

Familiarity with writing kernels for ML model execution.

Minimum Qualifications

Knowledge of GPU architecture and programming paradigms (e.g. Cuda/Triton or equivalent)

3-5 years working on MLIR-based compilers.

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

Fluent 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., 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.","responsibilities":"The successful candidate will perform development, performance analysis, and optimization of the MLIR compiler stack

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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