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

Apple; Cupertino (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Apple is looking for an ML Infrastructure Engineer to join its On-Device Machine Learning team in Cupertino, California. This role involves enhancing the MLIR-based compiler and collaborating with various teams to optimize execution across Apple devices. The ideal candidate has strong experience in compiler optimization, familiarity with machine learning frameworks, and deep knowledge of software fundamentals.

As part of a dynamic environment, you will contribute to transformative AI applications while working closely with cutting-edge technology, making a significant impact on the future of intelligent experiences across Apple's platforms.

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.

Responsibilities

• Inspire changes in MLIR-based compiler for improved runtime performance.
• Propose changes in MLIR for better feature support.
• Own core pieces of the compiler stack for heterogeneous compute.
• Work closely with hardware and software teams for optimization.

Skills

MLIR-based compilers

C++

PyTorch

Common ML model architectures

Tools

Swift

Job description

Summary

Imagine being at the forefront of an evolution where innovative AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation. Our team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding innovative architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across Camera, Siri, Health, Vision, and other core experiences, contributing to the overall Apple Intelligence ecosystem. If you are passionate about the technical challenges of running sophisticated ML models across all devices, from resource-constrained devices to powerful clusters, and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents a great opportunity to work on the next generation of intelligent experiences on Apple platforms. Our group is looking for an ML Infrastructure Engineer, with a focus on model compilation. The role entails working closely with model authoring, runtime, and performance teams to ensure that models can bring to bear the full capabilities of the hardware.

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

• 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.

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

Preferred Qualifications

• Familiarity with Swift.
• Familiarity with programming paradigms for the GPU, CPU, and Neural Engine.
• Familiarity with writing kernels for ML model execution.