Sequoia SW Optimization Engineer AI/ML
Summary
Engineer in Apple's Platform Architecture group who analyzes workloads to find performance bottlenecks on Apple Silicon (CPU, GPU, Neural Engine), prototypes and benchmarks algorithms, and feeds findings back to silicon and software teams. Core focus is performance optimization for AI/ML, graphics, and HPC using C/C++, Python, and frameworks like PyTorch.
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 all of our technologies work in unison, drive development of our renowned system-on-a-chip (SoC) architecture, and develop forward-looking prototype systems and software. Our team is driving performance enhancements in application and system software and developing novel algorithms to deliver integrated, highly optimized solutions based on Apple Silicon.
In this role, you will analyze existing and new workloads to identify performance bottlenecks in the hardware and/or software. Working with your colleagues, you'll address performance limitations and provide recommendations for Apple hardware and software improvements. In addition to working directly with developers, you will identify patterns of performance challenges on Apple silicon, emerging new usage models, and provide feedback to the silicon and software teams for potential improvements.
Minimum Qualifications
- Bachelor’s degree or equivalent job-related experience in Computer Engineering, Computer Science, or a related field.
- Experience in software development for at least one of the following hardware IPs: AI/ML HW accelerators, GPUs processing units, image/video encoders, or similar.
- Experience with AI/ML, graphics, or HPC performance benchmarks and workloads.
Preferred Qualifications
- M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field.
- 10+ years of relevant experience in software performance optimization, performance analysis tools, performance optimization process and development of efficient computational algorithms.
- Knowledge of computer architecture fundamentals.
- Proficiency in some of the C/C++ family programming languages, and scripting languages such as Python.
- Ability to prototype and benchmark algorithms on CPU, GPU, and Neural Engine platforms, analyze performance metrics, and create high-level complexity models, or develop targeted highly efficient low-level performance libraries for AI/ML accelerators or GPUs.
- Proficiency in popular AI/ML frameworks, such as PyTorch, and relevant software stacks.
- Knowledge of operating system internals and compiler technologies.
- Technical aptitude and curiosity, as well as ability to collaborate effectively with team members, partners, and stakeholders.