This role pays less than
57%
of similar roles. Most pay
$177,300–$241,750
— the shaded 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
2305 open roles
on FindRole.
Listed pay typically runs
$175,000–$280,000
across 1873 roles with salary data.
Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems joins the iOS Robotics team within the Wireless Technologies and Ecosystems group. This role focuses on the DockKit Framework to develop perception algorithms for robotics and intelligent systems. You will design and implement efficient ML inference pipelines on resource-constrained embedded hardware, transforming advanced models into optimized code for custom silicon, microcontrollers, DSPs, and ML accelerators. Day-to-day responsibilities include performing model quantization and pruning, developing low-level C/C++ software, and debugging performance bottlenecks across the hardware and software stack. You will utilize tools such as TensorFlow Lite, ONNX Runtime, and Core ML while leveraging Python for automation. The role addresses critical challenges in memory constraints, computational budgets, and real-time performance to deliver power-efficient edge AI solutions within a robotics context involving computer vision, NLP, or audio processing.
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 efficiency.
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 and software stack.
Evaluate and recommend embedded platforms, toolchains, and frameworks for on-device intelligence applications.
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 such as DSPs, NPUs, or 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
Machine Learning
Model Predictive Control
Reinforcement Learning
Optimal Control
Data Analysis
Firmware
Sensor Data
Battery Management
Prototyping
Machine Learning
Generative AI
Python
C++
Deep Learning Frameworks
Time-series Analysis
SoC Architecture
Silicon
Feature Engineering
System Architecture