Deep Learning Compiler Engineer
Apply NowCompany: Apple
Location: Sunnyvale, CA 94087
Description:
Summary
At Apple, we're on the cutting edge of delivering transformative experiences through Artificial Intelligence. If you're passionate about pushing the boundaries of AI and hardware optimization, we want you to join our team! As a Deep Learning Compiler Engineer on the Apple Neural Engine (ANE) team, you'll work to bring high-performance, low-power AI solutions to life on iconic Apple products like the Vision Pro, iPhone, iPad, Mac, and more. This is a dynamic opportunity to work with us in a creative, collaborative environment while developing groundbreaking technologies that will shape the future of computing!
We are looking for an engineer with deep expertise in compiler technology, and eager to tackle new challenges and responsibilities as the role evolves. As the position progresses, there will be opportunities to demonstrate leadership, influence key decisions, collaborate with and support other engineers, and help guide the direction of Apple's AI-driven capabilities across the ecosystem.
Are you ready to help us deliver the next groundbreaking Apple products?
Description
As a Deep Learning Compiler Engineer, you will:
Architect and develop the compiler for Apple's proprietary Neural Engine Accelerator, optimizing it for deep learning inference with a focus on performance, scalability, and power efficiency
Collaborate with cross-functional teams, including hardware and platform architecture teams, to bring new hardware silicon to market and ensure compiler support for next-gen features
Lead the design and implementation of complex compiler features, advancing both technical capabilities and strategic alignment across the team and company
Play an instrumental role in defining new compiler architecture approaches and optimizations, balancing trade-offs between performance, energy efficiency, and hardware constraints
Identify and drive initiatives that will improve the scalability and general performance of AI workloads on Apple hardware, contributing to the vision and roadmap of the Apple Neural Engine team
At Apple, we're on the cutting edge of delivering transformative experiences through Artificial Intelligence. If you're passionate about pushing the boundaries of AI and hardware optimization, we want you to join our team! As a Deep Learning Compiler Engineer on the Apple Neural Engine (ANE) team, you'll work to bring high-performance, low-power AI solutions to life on iconic Apple products like the Vision Pro, iPhone, iPad, Mac, and more. This is a dynamic opportunity to work with us in a creative, collaborative environment while developing groundbreaking technologies that will shape the future of computing!
We are looking for an engineer with deep expertise in compiler technology, and eager to tackle new challenges and responsibilities as the role evolves. As the position progresses, there will be opportunities to demonstrate leadership, influence key decisions, collaborate with and support other engineers, and help guide the direction of Apple's AI-driven capabilities across the ecosystem.
Are you ready to help us deliver the next groundbreaking Apple products?
Description
As a Deep Learning Compiler Engineer, you will:
Architect and develop the compiler for Apple's proprietary Neural Engine Accelerator, optimizing it for deep learning inference with a focus on performance, scalability, and power efficiency
Collaborate with cross-functional teams, including hardware and platform architecture teams, to bring new hardware silicon to market and ensure compiler support for next-gen features
Lead the design and implementation of complex compiler features, advancing both technical capabilities and strategic alignment across the team and company
Play an instrumental role in defining new compiler architecture approaches and optimizations, balancing trade-offs between performance, energy efficiency, and hardware constraints
Identify and drive initiatives that will improve the scalability and general performance of AI workloads on Apple hardware, contributing to the vision and roadmap of the Apple Neural Engine team