On-Device ML Integration Engineer, Graphics, Games and Machine Learning

We are seeking an ML Integration Engineer. In this role, you will ensure Apple's inference stack allows integrating ML workflows end-to-end with excellent user experience, flawless functionality, and maximum performance. This role is far reaching and you will partner with teams across our ML deployment stack, from ML model developers to runtime engineers, as you ensure the best experience, functionality, and maximum performance for ML workflows. The scope of work is wide, spanning model-side updates, ML frameworks export, custom kernels, compiler optimization, and development of analysis and debugging tools.

As a power user of Apple's ML infrastructure, you will also help spearhead the integration of the latest and most capable models with strong, competitive performance across hardware targets, showcasing the practical power of Apple's authoring and runtime APIs. This role offers the unique opportunity to shape how ML developers experience Apple's end-to-end inference stack, from model creation to deployment.","responsibilities":"Ensure functional and performant integration of Apple's ML models across the inference stack.

Integrate Apple's ML tools into internal and external model repositories to demonstrate and stress-test model ingestion with peak efficiency and performance.

Develop optimizations across the pipeline, including model-level transformations, custom operations, or compiler optimizations to improve inference efficiency.

Spearhead the integration of the cutting-edge ML models with peak performance, using these examples to validate or improve Apple's inference stack.

Do you have experience in Machine learning libraries?, Experience with C++, Swift.

Experience with GPU kernel optimizations.

Experience with MLIR/LLVM or similar compiler toolchains.

Familiarity with Hugging Face or other model repositories.

Minimum Qualifications

Bachelors in Computer Sciences, Engineering, or related discipline.

Proficient in Python programming. Some familiarity with C++ is required.

Proficiency in at least one ML authoring framework, such as PyTorch, MLX, and JAX.

Understanding of ML fundamentals, including common architectures such as Transformers.

Understanding of GPU programming paradigms.

Strong communication skills, including ability to communicate with cross-functional audiences.

4.14.1 out of 5 stars Cupertino, CA $181,100 - $318,400 a year, Pulled from the full job description

• Employee stock purchase plan
• Health insurance
• Retirement plan
• Dental insurance
• RSU, 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 $181,100 and $318,400, 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 cutting-edge 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 cutting-edge 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 an exciting opportunity to work on the next generation of intelligent experiences on Apple platforms.