Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems

What you'll do

• Design and implement efficient ML inference pipelines; optimize models for performance, memory, and power.
• Develop and integrate low-level C/C++ software for microcontrollers, DSPs, and ML accelerators; debug performance and power bottlenecks.
• Collaborate across research, hardware, and platform teams, and evaluate embedded platforms, toolchains, and ML frameworks.

What they ask for

• Bachelor’s degree plus 3+ years of experience, or Master’s degree plus 2+ years of experience, in CS, EE, or a related technical field.
• Familiarity with C/C++ embedded development, including RTOS, microcontrollers, and low-level hardware interactions.
• Ability to optimize and deploy ML models on resource-constrained edge devices using techniques such as quantization and pruning, and frameworks such as TensorFlow Lite, ONNX Runtime, or Core ML.
• Strong analytical and debugging skills for performance bottlenecks across hardware, firmware, and ML inference.

Nice to have

• Experience with ML inference hardware acceleration (DSPs, NPUs, ASICs), embedded Linux or other RTOS in production, or relevant open-source contributions or publications.
• Knowledge of neural network architectures, computer vision, NLP, or audio processing for embedded/robotics; proficiency with Python.

Pay

$142,300 and $263,300 (USD base salary range)

Tools

C/C++, RTOS, TensorFlow Lite, ONNX Runtime, Core ML, DSPs, NPUs, ASICs, Python

AI focus

Deploy and optimize low-power, on-device ML models for embedded hardware and robotics, including inference pipelines, quantization, and pruning.