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ML Framework (MetalLM) Engineer

Cupertino,California,United States

Software and Services

Apple’s ML Frameworks team in GPU, Graphics and Displays org provides GPU acceleration for popular Machine learning libraries such as TensorFlow, PyTorch and JAX using Metal runtime and device backend. It optimizes compute performance with kernels and computational graphs that are fine-tuned for the unique characteristics of each Metal GPU family. We are always looking for exceptionally dedicated individuals to grow our outstanding team.

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: * Design and develop compiler based optimizations for Metal backend in ML frameworks such as torch.compile for PyTorch * Work on cutting-edge ML inference framework project and optimize code for efficient and scalable ML inference using distributed techniques * Implement features of Metal device backend for ML training acceleration technologies * Work with Core teams of PyTorch, JAX or Tensorflow to provide Metal runtime and device backend support * Tune GPU-accelerated training across products. * Performing in-depth analysis, compiler and kernel level optimizations to ensure the best possible performance across hardware families. * Intended deliverables: * GPU accelerated ML Frameworks technology * Optimized ML training across products. If 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 Distributed training or inference techniques

• GPU compute programming models & optimization techniques

• Experience with system level programming and computer architecture

Key Qualifications

Preferred Qualifications

• Contributions to an AI framework such as PyTorch, JAX or Tensorflow is a plus

• Experience with graph compilers such as Triton, OpenXLA or LLVM/MLIR is a plus

• Good understanding of machine learning fundamentals

Education & Experience

Additional Requirements

Pay & Benefits

• 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 $143,100 and $264,200, 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 (https://www.apple.com/careers/us/benefits.html) 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 take affirmative action to ensure 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. (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf)

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