ML Compute Acceleration Engineer

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Apple’s Compute Frameworks team in GPU, Graphics and Displays org provides a suite of high-performance data parallel algorithms for developers inside and outside of Apple for iOS, macOS and Apple TV. Our efforts are currently focused in the key areas of linear algebra, image processing, machine learning, along with other projects of key interest to Apple. We are always looking for exceptionally dedicated individuals to grow our outstanding team to lay the foundation of technologies like Apple Intelligence.
Our team is seeking extraordinary machine learning and GPU programming engineers who are passionate about providing robust compute solutions for accelerating machine learning networks on Apple Silicon using GPU and Neural Engine. Role has the opportunity to influence the design of compute and programming models in next generation GPU and Neural Engine architectures. - Responsibilities: - Adding optimizations in machine learning computation graph. - Defining and implementing APIs in Metal Performance Shaders Graph, investigating new algorithms. - Developing and maintaining MLIR dialect in Apple and open source with upgrades using latest LLVM. - Performing in-depth analysis, compiler and kernel level optimizations to ensure the best possible performance across hardware families. - Tune GPU and Neural Engine accelerated compute across products. - Tuning the cost model and optimizing runtime dispatch to multiple IPs to get best performance on Apple Silicon. - Intended deliverables: - GPU Compute acceleration technology. - Apple Intelligence implementation and acceleration. - Optimized compute graphs across products. If this sounds of interest, we would love to hear from you!
  • Proven programming and problem-solving skills.
  • Good understanding of machine learning fundamentals.
  • GPU compute programming models & optimization techniques.
  • GPU compute framework development, maintenance, and optimization.
  • Experience with system level programming and computer architecture.
  • Experience with high performance parallel programming, GPU programming or LLVM/MLIR compiler infrastructure is a plus.
  • Background in mathematics, including linear algebra and numerical methods.
  • Strong communication and collaboration skills.
  • Strong background of building high performance, production quality software on schedule.
  • Experience with compiler technologies.
  • Experience with adding computational graph support, runtime or device backend to Machine learning libraries (TensorFlow, PyTorch or JAX) support is a 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 $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 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.

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