Machine Learning Compiler Engineer
Apple
Machine Learning Compiler Engineer
Job Description
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 Machine 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! Are you ready to help us deliver the next groundbreaking Apple products? As a Machine 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
Locations
- Sunnyvale, California, United States 94085
Salary
Estimated Salary Rangemedium confidence
30,000,000 - 80,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learning Compiler Engineeringintermediate
- Architecting compilersintermediate
- Developing compilersintermediate
- Optimizing for deep learning inferenceintermediate
- Optimizing for performanceintermediate
- Optimizing for scalabilityintermediate
- Optimizing for power efficiencyintermediate
- Collaborating with cross-functional teamsintermediate
- Leading design and implementation of compiler featuresintermediate
- Defining compiler architecture approachesintermediate
- Compiler optimizationsintermediate
- Balancing performance and energy efficiency trade-offsintermediate
- Balancing hardware constraintsintermediate
- Improving scalability of AI workloadsintermediate
- Improving general performance of AI workloadsintermediate
- Hardware optimizationintermediate
- AI workload optimizationintermediate
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field with 3 years of relevant experience (experience, 3 years)
- Experience with program analysis and IR (Intermediate Representation), or programming language design, particularly with MLIR and LLVM (experience)
- Proven expertise in compiler design and architecture, including deep experience with front-end and middle-end optimizations, register allocation, and back-end code generation (experience)
- High-level proficiency in C++ and experience working with large, complex software systems (experience)
Preferred Qualifications
- Master's or PhD degree in Computer Science, Computer Engineering, or a related field (degree in phd degree in computer science)
- Demonstrated ability to ship high-quality production software (experience)
- Strong communication skills and ability to collaborate effectively across teams and functions (experience)
- Experience optimizing compilers for distributed, parallel, or heterogeneous execution environments, with a solid understanding of shared memory, synchronization, and multi-threading techniques (experience)
- Expertise in neural network inference on specialized SoCs or GPUs, and knowledge of deep learning frameworks and tools (experience)
- Familiarity with Just-in-Time (JIT) compilation and dynamic optimization techniques for real-time code execution (experience)
Responsibilities
- As a Machine 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
Tags & Categories
Machine Learning Compiler Engineer
Apple
Machine Learning Compiler Engineer
Job Description
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 Machine 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! Are you ready to help us deliver the next groundbreaking Apple products? As a Machine 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
Locations
- Sunnyvale, California, United States 94085
Salary
Estimated Salary Rangemedium confidence
30,000,000 - 80,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learning Compiler Engineeringintermediate
- Architecting compilersintermediate
- Developing compilersintermediate
- Optimizing for deep learning inferenceintermediate
- Optimizing for performanceintermediate
- Optimizing for scalabilityintermediate
- Optimizing for power efficiencyintermediate
- Collaborating with cross-functional teamsintermediate
- Leading design and implementation of compiler featuresintermediate
- Defining compiler architecture approachesintermediate
- Compiler optimizationsintermediate
- Balancing performance and energy efficiency trade-offsintermediate
- Balancing hardware constraintsintermediate
- Improving scalability of AI workloadsintermediate
- Improving general performance of AI workloadsintermediate
- Hardware optimizationintermediate
- AI workload optimizationintermediate
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field with 3 years of relevant experience (experience, 3 years)
- Experience with program analysis and IR (Intermediate Representation), or programming language design, particularly with MLIR and LLVM (experience)
- Proven expertise in compiler design and architecture, including deep experience with front-end and middle-end optimizations, register allocation, and back-end code generation (experience)
- High-level proficiency in C++ and experience working with large, complex software systems (experience)
Preferred Qualifications
- Master's or PhD degree in Computer Science, Computer Engineering, or a related field (degree in phd degree in computer science)
- Demonstrated ability to ship high-quality production software (experience)
- Strong communication skills and ability to collaborate effectively across teams and functions (experience)
- Experience optimizing compilers for distributed, parallel, or heterogeneous execution environments, with a solid understanding of shared memory, synchronization, and multi-threading techniques (experience)
- Expertise in neural network inference on specialized SoCs or GPUs, and knowledge of deep learning frameworks and tools (experience)
- Familiarity with Just-in-Time (JIT) compilation and dynamic optimization techniques for real-time code execution (experience)
Responsibilities
- As a Machine 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
Tags & Categories