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 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 and on-device ML inference.

The role entails deep analysis of ML inference from the SW stack and low level

drivers to HW debug involving CPU, GPU, Apple Neural Engine, system memory and

power.

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, 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 and memory,

compilers and HW drivers.

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 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 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 on-device ML stack, such as TFLite, ONNX, ExecuTorch, etc.

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

Experience with Apple’s App development framework such as Xcode, Swift,

Objective-C Experience with any compiler stack (MLIR/LLVM/TVM etc.)