below marketmost similar roles pay hereabove market
This role pays less than
55%
of similar roles. Most pay
$177,300–$241,750
— the blue band above.
At the midpoint, this role pays about
$203k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3564 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2760 roles with salary data.
The Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems joins the iOS Robotics team to expand the DockKit Framework. This role focuses on developing perception algorithms and intelligent experiences for robotics and intelligent systems. You will design and implement efficient ML inference pipelines, transforming advanced algorithms into optimized, power-efficient code for custom silicon and microcontrollers. Daily responsibilities include optimizing neural networks through quantization and pruning, developing low-level C/C++ software for DSPs and ML accelerators, and debugging performance bottlenecks across the hardware and software stack. You will utilize tools and frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML. The work involves solving complex challenges related to memory constraints, computational budgets, and real-time performance to deliver high-quality edge AI solutions on resource-constrained embedded hardware.
Design and implement efficient ML inference pipelines on resource-constrained embedded hardware.
Optimize neural network models using quantization and pruning for performance, memory, and power.
Develop and integrate C/C++ low-level software for microcontrollers, DSPs, and ML accelerators.
Analyze and debug performance bottlenecks and power consumption across the hardware/software stack.
Evaluate and recommend embedded platforms, toolchains, and ML frameworks for on-device intelligence.
Transform advanced ML algorithms into optimized code for custom silicon and microcontrollers.
What we're looking for
Bachelor’s degree with 3+ years experience or Master’s degree with 2+ years experience in CS, EE, or a related technical field.
Proficiency in C/C++ for embedded systems development, including RTOS, microcontrollers, and low-level hardware interactions.
Proven ability to optimize and deploy ML models for resource-constrained edge devices using quantization, pruning, and frameworks like TensorFlow Lite, ONNX Runtime, or Core ML.
Strong analytical and debugging skills to resolve performance bottlenecks across hardware, firmware, and ML inference.
Experience with ML inference hardware acceleration (DSPs, NPUs, ASICs) (preferred).
Familiarity with diverse neural network architectures and training methodologies for efficient edge deployment (preferred).
Knowledge of computer vision, NLP, or audio processing in an embedded/robotics context (preferred).
Experience with embedded Linux or other RTOS in a production environment (preferred).
Python
C++
PyTorch
CUDA
GPU Kernel Optimization
Distributed Training
Data Pipelines
Inference Serving
Computer Architecture
Model Deployment
Data Ingestion
Profiling
Debugging
Claude