Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems
Core
Deploy efficient, low-power ML models directly onto embedded hardware for robotics and intelligent systems.
Role type
Senior IC embedded machine learning engineer
Builds
On-device intelligent experiences for iOS robotics and accessories
Domain
Consumer electronics, robotics, embedded systems
Deliverable
production ML models
Required skills
C/C++ for embedded systems, neural network optimization (quantization, pruning), ML inference on resource-constrained devices, low-level software development for microcontrollers/DSPs, performance and power analysis
Preferred skills
ML inference hardware acceleration (DSPs, NPUs, ASICs), embedded Linux/RTOS, computer vision/NLP/audio processing in embedded context, Python for automation
Technologies
TensorFlow Lite, ONNX Runtime, Core ML
Responsibilities
Design and implement efficient ML inference pipelines on resource-constrained embedded hardware; Optimize neural network models for performance, memory, and power on edge devices; Develop and integrate robust C/C++ low-level software for deploying ML models on microcontrollers, DSPs, and ML accelerators; Analyze and debug performance bottlenecks and power consumption across the hardware/software stack for ML workloads; Collaborate with ML researchers, hardware engineers, and platform teams to deliver high-quality, power-efficient edge AI solutions; Evaluate and recommend embedded platforms, toolchains, and ML frameworks for on-device intelligence applications
Seniority
Senior, hands-on IC
