Machine Learning Architect, Platform Architecture

Apple Inc.

Cupertino, Northern (CA, KY)

On-site

USD 216,000 - 325,000

Full time

8 days ago
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Benefits offered by this job

Stock programs
Relocation eligibility
Educational reimbursement

Job summary

Apple Inc. is seeking a senior role within the Platform Architecture group in Cupertino to map ML workloads onto Apple silicon and develop performance models.

The candidate will evaluate optimization techniques and influence hardware and algorithm directions while collaborating with ML, system, and IP teams. Responsibilities include implementing ML workloads on Neural Engine, GPU, and CPU; developing high-level models; and analyzing trade-offs to guide future hardware design.

Qualifications

  • Experience in C/C++ and/or Python.
  • Experience with ML frameworks and efficient ML algorithm implementations.
  • Experience with hardware IPs: ML HW accelerators, GPU/CPU, image/video processors or similar.

Responsibilities

  • Create optimized implementations of ML workloads on Apple silicon including Neural Engine, GPU, and CPU.
  • Collaborate with IP and SoC architecture teams to develop performance models and simulations of future hardware.
  • Collaborate with system teams to create high-level performance models of emerging ML techniques and analyze system architecture trade-offs.
  • Evaluate emerging ML model optimization techniques through experimentation and analysis; propose new ideas to inform hardware and algorithm direction.

Skills

C/C++
Python
ML frameworks

Education

MS or PhD in EE/CE/CS

Tools

PyTorch

Job description

Cupertino, California, United States Hardware

At Apple, our Platform Architecture group is responsible for connecting our hardware and software into one unified system. You’ll collaborate with engineers across Apple to design how our technologies work in unison, drive development of our renowned system-on-a-chip architecture and forward-looking prototype systems. Our team works at the intersection of ML applications and Apple silicon architecture. We collaborate with SoC/IP architecture, system, software, and algorithm teams to develop integrated, highly optimized solutions for machine learning applications.

Description

In this role, you will explore different ways of mapping ML workloads to Apple silicon and develop performance models/simulations. Your work will inform and validate architecture decisions. You will critically evaluate ML model optimization techniques from the literature, analyzing what works and why, and proposing new ideas that build on what you learn. You will gain insights on how to make workloads run efficiently on our SoCs and provide guidance to software and algorithm teams.

Responsibilities
  • Create optimized implementations of ML workloads on Apple silicon including Neural Engine, GPU, and CPU.
  • Collaborate with IP and SoC architecture teams to develop performance models and simulations of future hardware.
  • Collaborate with system teams to create high-level performance models of emerging ML techniques and analyze system architecture trade-offs.
  • Evaluate emerging ML model optimization techniques through experimentation and analysis; propose new ideas to inform hardware and algorithm direction.
Minimum Qualifications
  • Experience in C/C++ and/or Python
  • Experience in hardware IPs: ML HW accelerators, GPU/CPU, image/video processors or similar.
  • Experience with ML frameworks (e.g. PyTorch) and efficient implementations of machine learning algorithms
Preferred Qualifications
  • MS or PhD in EE/CE/CS or related field
  • 20+ years of relevant experience
  • Experience in optimizing and deploying ML models and/or runtime frameworks in production inference/training environments
  • Experience designing experiments to evaluate ML model optimization techniques
  • Ability to prototype algorithms on CPU/GPU/Neural Engine, analyze performance metrics, and create high-level complexity models
  • Verbal and written communication skills for collaborating with partner teams
  • Understanding of compilers

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 $216,200 and $324,800, 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.

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