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Machine Learning Compiler Engineer (m/f/d)

Apple · Munich

External listingfull-time4 days ago

About The Role

At Apple, we're on the cutting edge of delivering transformative experiences through Artificial Intelligence. If you're passionate about pushing the boundaries of AI and hardware optimization, we want you to join our team!

## Description

As a Machine Learning Compiler Engineer on the Apple Neural Engine (ANE) team, you'll work to bring high-performance, low-power AI solutions to life on iconic Apple products like the Vision Pro, iPhone, iPad, Mac, and more. This is a dynamic opportunity to work with us in a creative, collaborative environment while developing groundbreaking technologies that will shape the future of computing.

Are you ready to help us deliver the next groundbreaking Apple products?

## Minimum qualifications

Bachelor’s degree in Computer Science, Computer Engineering, or a related field with 3 years of relevant experience

Experience with program analysis and IR (Intermediate Representation), or programming language design, particularly with MLIR and LLVM

Proven expertise in compiler design and architecture, including deep experience with front-end and middle-end optimizations, register allocation, and back-end code generation

High-level proficiency in C++ and experience working with large, complex software systems

Proficiency in English

## Preferred qualifications

Master's or PhD degree in Computer Science, Computer Engineering, or a related field

Demonstrated ability to ship high-quality production software

Strong communication skills and ability to collaborate effectively across teams and functions

Experience optimizing compilers for distributed, parallel, or heterogeneous execution environments, with a solid understanding of shared memory, synchronization, and multi-threading techniques

Expertise in neural network inference on specialized SoCs or GPUs, and knowledge of deep learning frameworks and tools

Familiarity with Just-in-Time (JIT) compilation and dynamic optimization techniques for real-time code execution

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