SW ML Optimization Engineer

At Apple our Platform Architecture group is responsible for connecting our hardware and software into one unified system. Youll collaborate with engineers across Apple to design how all of our technologies work in unison drive development of our renowned system-on-a-chip 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 this role you will analyze existing and new workloads to identify performance bottlenecks in the hardware and/or software. Working with your colleagues you will address performance limitations and provide recommendations for Apple hardware and software 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.

Profile and analyze AI/ML workloads across Apple Silicon compute engines (GPU ANE and CPU) to help identify performance bottlenecks working alongside senior targeted microbenchmarks to characterize the strengths weaknesses and usage patterns of different devices and and tune optimized implementations of software workloads on Apple Silicon (GPU and CPU) learning to apply the latest instruction sets and to the development of forward-looking AI/ML workloads that represent where the industry is with system and tools teams to prototype performance experiments and help build performance analysis instruments libraries and toward a system-wide understanding of the stackfrom high-level APIs down to the hardware.

Bachelors degree in Computer Science Computer Engineering Mathematics Electrical Engineering or a related quantitative field (or equivalent practical experience).nHands-on experience with GPU or parallel programminge.g. Metal OpenCL CUDA or similarthrough coursework personal projects internships or with profiling/performance analysis tools (e.g. Xcode Instruments VTune Nsight Compute or equivalent) and basic performance analysis experience in Python C or C.

Solid foundation in mathematics algorithms and/or computer architecture writing or tuning compute kernels (e.g. GEMM attention or other numerically intensive routines).nExposure to ML frameworks such as PyTorch and to AI/ML graphics or HPC workloads and or projects involving parallel computing numerical methods signal processing or performance in (or exposure to) the deeper stack - drivers firmware compilers or low-level in Apple Silicon and its frameworks (Metal MLX Core ML).nCuriosity about hardware/software co-design and a demonstrated drive to learn communication skills and the ability to collaborate effectively across teams.