On-device ML Performance Engineer, Graphics, Games and Machine Learning

The On-Device Machine Learning team at Apple is responsible for enabling the

Research to Production lifecycle of cutting edge machine learning models that

power magical user experiences on Apple’s hardware and software platforms. Apple

is the best place to do on-device machine learning, and this team sits at the

heart of that discipline, interfacing with research, SW engineering, HW

engineering, and products. The On-device ML Performance team has the

responsibility to analyze latency, memory, power and numerical correctness of

the latest machine learning models running on Apple SoC’s, and to make Apple’s

ML software stack take full advantage of the capabilities in Apple’s ML

accelerators. The work from this cross functional team enables model developers’

decisions to optimize performance via advanced techniques such as different

model authoring techniques, quantization, sparsity, performance and accuracy

tradeoffs. The work of this team impacts all new Apple HW and ML Inference on

them. Our group is looking for an On-device ML Performance Engineer, with

technical expertise in computer architecture, performance, memory, power, ML

model architectures, ML frameworks such as PyTorch, and on-device ML inference.

The role entails deep analysis of ML models and their architecture, the

implementation of the models in the ML SW stack for optimum performance, power

and memory usage, and debug involving the performance and power consumption of

CPU, GPU, and Apple Neural Engine.

DESCRIPTION

As an engineer in this role, you will be primarily focused on analyzing and

optimizing the performance of the latest ML models on the latest iPhones and

Mac’s. You will work with models created by the most popular ML frameworks

(PyTorch, MLX, etc) and will analyze the inference of those models on device to

ensure the stack achieves full machine performance on Apple Silicon. The role

also includes scripting, coding, model import and conversions, and generation of

utilities and debug tools to extract, analyze, and report performance and power

related metrics for Apple HW. The ideal candidate will have a passion for ML

model architectures and ML inference, deep knowledge of GPU and CPU, computer

architecture, compilers, and has a natural inclination toward innovation and

exploration.

MINIMUM QUALIFICATIONS

Experience with ML inference, quantization, performance and accuracy Familiarity

and experience with the most popular ML architectures (e.g. LLM’s, Diffusion

models, CNN’s) A passion to explore and learn about the latest advances in ML

model design and architecture, particularly as related to model implementation

on HW and on-device inference Familiarity with Operating Systems, embedded

systems, and CPU/GPU/SoC/Memory HW architectures Highly proficient in Python/C++

and shell scripting Familiarity with Linux or macOS Exceptional clarity in

verbal and written communication, including the ability to summarize, present

and lead discussions in larger groups

PREFERRED QUALIFICATIONS

Masters or PhDs in Computer Science or relevant disciplines. Experience with

Apple’s CoreML, MPS Graph, Metal Performance Shader’s or MLX frameworks

Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.)

Experience with implementation of high performance compute kernels for CPU, GPU

or AI Accelerators Experience with Apple’s App development framework such as

Xcode, Swift, Objective-C Experience with any on-device ML stack, such as

TFLite, ONNX, ExecuTorch, etc. Experience with any compiler stack (MLIR/LLVM/TVM

etc.)