On-Device ML Infrastructure Engineer (CoreML Runtime), Graphics, Games and Machine Learning

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 ML Runtime for execution on-device. In this role, you will build the world’s most advanced ML graph compilation and runtime system, capable of optimizing and delivering ML models efficiently on Apple products and services.

Responsibilities

Architect and maintain the on-device graph compiler, runtime, and kernels for delivering ML operators.

Develop production-critical system software for implementing ML models on Apple Silicon

Proactively identify and resolve functionality gaps.

Optimize model execution for various system objectives like performance, energy efficiency, and thermal management.

We are seeking an ML Infrastructure Engineer with a specific focus on graph compilers and runtimes. If you are a highly motivated software engineer who is creative, versatile, and passionate about machine learning operator primitives, common compiler optimizations, runtimes, and system software engineering in the fast-paced and dynamic field of machine learning, this could be a fantastic role for you., Masters or equivalent experience in Computer Sciences, Engineering, or related subject area.

Highly proficient in C++ or Swift. Familiarity with Python.

Experience with any compiler stack (MLIR/LLVM/TVM/…).

Familiarity with Operating Systems, embedding programming, parallel programming.

Sound understanding of ML fundamentals, including common architectures such as Transformers.

Good communication skills, including ability to communicate with multi-functional audiences.

Preferred Qualifications

Experience with any on-device ML stack, such as TFLite, ONNX, ExecuTorch, etc.

Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.) is a strong plus.

Experience with accelerators, GPU programming is a strong plus.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Imagine being at the forefront of an evolution where powerful 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 a top destination for on-device 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 on resource-constrained devices and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents an incredible opportunity to work on the next generation of intelligent experiences on Apple platforms.