ML Frameworks Engineer — GPU/Metal Inference on Apple Silicon

Apple Inc.; Cupertino (CA); On-site; USD 150,400 - 225,300; Full time

Benefits offered by this job

Medical and dental coverage

Retirement benefits

Stock programs / RSUs

Discounted products and services

Education reimbursement

Job summary

Apple’s ML Frameworks (MetalLM) team is advancing high-performance, distributed inference for GenAI apps on Private Cloud Compute. You will work on custom server hardware that brings Apple silicon power and security to the data center, and contribute to GPU-accelerated ML training frameworks using Metal runtimes.

We seek engineers with a systems background who are passionate about scalable, production-grade solutions for high-throughput GPU execution and performance optimization across server

Qualifications

• 3+ years of programming and problem-solving experience with C/C++/ObjC.
• Experience with GPU kernel development & optimizations using Metal, CUDA etc.
• Experience with Distributed training or inference techniques.

Responsibilities

• Work on cutting-edge ML inference framework project and optimize code for efficient and scalable ML inference using distributed compute strategies.
• Develop kernel and compiler level optimizations and perform in-depth analysis to ensure best performance across server hardware families.
• Apply advanced model optimization techniques including quantization, compression, and others to maximize throughput and minimize latency.
• Collaborate closely with hardware, compiler, and systems teams to align software performance with hardware capabilities.
• Analyze and improve performance metrics such as end-to-end latency and memory footprint.

Skills

C/C++/ObjC

System-level programming

Distributed training/inference

GPU kernel optimization

Tools

Metal

CUDA

GPU programming models

Job description

Cupertino, California, United States Software and Services

Apple’s ML Frameworks (MetalLM) team in GPU, Graphics and Machine Learning works on enabling Apple Intelligence through high-performance, distributed inference of GenAI applications (such as LLMs) on Private Cloud Compute. You will get to work on custom-built server hardware that brings the power and security of Apple silicon to the data center.Team also works on GPU acceleration of ML Training frameworks such as PyTorch and JAX using Metal runtime and device backend. We are looking for engineers with systems background who are deeply passionate about building scalable, efficient, and production-grade solutions tailored for high-throughput GPU execution.

Description

Our team is seeking extraordinary machine learning and GPU programming engineers who are passionate about providing robust compute solutions for accelerating Machine learning libraries on Apple Silicon. Role has the opportunity to influence the design of compute and programming models in next generation GPU architectures.* Responsibilities:Work on cutting-edge ML inference framework project and optimize code for efficient and scalable ML inference using distributed compute strategies such as data, tensor, pipeline and expert parallelism.Develop kernel and compiler level optimizations and perform in-depth analysis to ensure the best possible performance across Server hardware families.Apply advanced model optimization techniques including speculation, quantization, compression, and others to maximize throughput and minimize latency.Collaborate closely with hardware, compiler, and systems teams to align software performance with hardware capabilities.Analyze and improve performance metrics such as end-to-end latency, TTFT, TBOT, memory footprint, and compute efficiency.Implement features of Metal device backend for ML training acceleration technologiesIf this sounds of interest, we would love to hear from you!

Minimum Qualifications

• 3+ years of programming and problem-solving experience with C/C++/ObjC
• Experience with GPU kernel development & optimizations using compute programming models such as Metal, CUDA etc.
• Experience with system level programming and computer architecture
• Experience with Distributed training or inference techniques

Preferred Qualifications

• Experience with graph compilers such as Triton, OpenXLA or LLVM/MLIR is a plus
• Contributions to an AI framework such as PyTorch, JAX or Tensorflow is a plus
• Good understanding of machine learning fundamentals

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 $225,300, 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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.