2 months ago
Responsibilities
- Analyze latency, memory, power, and numerical correctness of ML inference on Apple hardware.
- Optimize ML model performance across Apple CPUs, GPUs, Neural Engine, system memory, and ML accelerators.
- Investigate inference behavior from the software stack and low-level drivers through hardware debugging.
- Create scripts, utilities, and debugging tools to extract, analyze, and report performance and power metrics.
- Collaborate with research, software engineering, hardware engineering, and product teams and lead technical discussions.
Requirements
- Experience with ML inference, quantization, and performance-versus-accuracy tradeoffs.
- Familiarity with ML architectures including large language models, diffusion models, and convolutional neural networks.
- Familiarity with operating systems, embedded systems, CPU/GPU hardware architectures, computer architecture, memory, compilers, and hardware drivers.
- Highly proficient in Python, C++, and shell scripting.
- Familiarity with Linux or macOS.
- Strong verbal and written communication skills, including presenting and leading discussions with larger groups.
- Preferred qualifications include a master’s or PhD in computer science or a related discipline.
- Preferred experience includes CoreML, MPS Graph, Metal Performance Shaders, MLX, TFLite, ONNX, ExecuTorch, PyTorch, TensorFlow, JAX, Xcode, Swift, Objective-C, MLIR, LLVM, or TVM.
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.