Deep Learning Compiler Engineer

Cupertino, CA
  • Job Code
    200033213

Summary

We live in a mobile and device driven world, where Deep Learning technology enables a new class of applications.

Are you passionate about enabling unique user experience such as Face ID, Animoji, AR games? Imagine the countless possibilities powered by Artificial Intelligence

In the Video Engineering team, we are dedicated in providing hardware acceleration using the new proprietary Apple Neural Engine SOC to enable real time, low power and high performance execution of Deep Learning workloads.

Our success is the result of very talented people working in an environment which cultivates creativity, partnership and cross functional collaboration.

These elements come together to make Apple an amazing environment for motivated people to do the greatest work of their lives.

Will you help us design the next generation of revolutionary Apple Products?

Key Qualifications

  • Experience with front-end, middle-end optimizations, scheduling, register allocation, or back-end code generation
  • Real passion for developing and optimizing compilers for modern architectures
  • Experience with neural networks inference on dedicated SOC or GPU
  • High level C++ programming expertise
  • Excellent problem solving and debugging skills
  • Experience building high quality production software
  • Excellent communication and collaboration skills
  • 2 years plus, relevant experience

Description

We develop compiler technology to accelerate deep learning applications for Apple products. You will:
- Bring up new hardware technology and add support in the compiler for these hardware features
- Work closely with the driver/firmware teams to integrate HW acceleration in our software stack
- Work on runtime performance optimization
- Craft tests and help bringing the compiler to production quality

Education & Experience

Masters's degree or higher in Computer Science or equivalent field.

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Deep Learning Compiler Engineer

Apple, Inc.
Cupertino, CA

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