Machine Learning Engineer Platform Architecture
@ Apple

Cupertino, California, United States
$150,000
On Site
Full Time
Posted 24 days ago

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Job Details

Overview

At Apple, our Platform Architecture team integrates hardware and software into a unified system. As a Machine Learning Engineer Platform Architecture, you will collaborate with various engineering teams to drive the development of our system-on-a-chip architecture and develop forward-looking prototype systems.

Role Responsibilities

You will explore different ways of mapping ML workloads to Apple silicon, develop performance models and simulations, and inform architecture decisions. Your insights will help optimize workload performance on our SoCs and improve communication with software and algorithm teams.

Minimum Qualifications

  • Bachelor’s degree
  • Proficiency in C/C++ and/or Python
  • Understanding of computer architecture fundamentals
  • Domain knowledge in ML hardware accelerators or similar processing units

Preferred Qualifications

  • MS/PhD in EE/CE/CS or equivalent experience
  • Experience with ML frameworks such as PyTorch
  • Expertise in optimizing and deploying ML models in production
  • Experience in creating SoC or IP performance models/simulations
  • Strong communication skills for cross-team collaboration
  • Ability to prototype algorithms and analyze performance metrics
  • Understanding of compiler frameworks and technologies

Key skills/competency

machine learning, platform architecture, SoC, simulation, performance modeling, prototype, C/C++, Python, ML frameworks, optimization

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How to Get Hired at Apple

🎯 Tips for Getting Hired

  • Research Apple culture: Understand Apple mission, values, and recent news.
  • Customize your resume: Highlight ML and architecture projects.
  • Prepare technical case studies: Demonstrate performance modeling skills.
  • Practice interview insights: Review cross-team collaboration examples.

📝 Interview Preparation Advice

Technical Preparation

Review C/C++ and Python programming.
Study computer architecture fundamentals.
Practice performance simulation exercises.
Learn ML framework implementations.

Behavioral Questions

Describe a cross-team collaboration experience.
Explain your approach to problem-solving.
Discuss a challenging technical project.
Illustrate how you communicate complex ideas.

Frequently Asked Questions