Edge AI Software Engineer

Job Summary

The Edge AI Software Engineer is responsible for driving AI capabilities on resource constrained embedded and edge platforms, enabling the next generation intelligent evolution of wireless communication modules and IoT products. Beyond traditional embedded and cellular module software, this role focuses on deep integration of AI models with embedded platforms, including MCU, RTOS, and Embedded Linux environments, reporting to Software Manager.

This position contributes to the adaptation, optimization, and deployment of AI and large scale models on constrained devices, balancing compute capability, memory footprint, power consumption, real time behavior, and system stability.

In addition, the role drives the adoption and production deployment of LLM based Agent frameworks into daily engineering workflows, improving development efficiency and engineering productivity. The role requires close collaboration with AI algorithm, module platform, system architecture, and product teams to deliver production ready edge AI solutions.

Objectives & Responsibilities

• Adapt, optimize, and deploy AI models on embedded and edge platforms, including model pruning, compression, quantization, distillation, and runtime integration for production use.
• Design and evolve AI model architectures based on business scenarios and deployment constraints, balancing accuracy, latency, memory usage, power consumption, and system stability on resource limited platforms.
• Develop and maintain embedded AI software frameworks and inference pipelines, supporting lightweight runtimes such as TensorFlow Lite, TensorFlow Lite Micro, PyTorch Mobile, ONNX Runtime, and similar engines across MCU, RTOS, and Embedded Linux platforms.
• Lead inference acceleration and performance optimization on embedded systems, leveraging platform capabilities and heterogeneous resources (CPU, DSP, NPU, GPU) to continuously improve edge side AI efficiency.
• Drive the adoption and production integration of LLM based Agent frameworks, embedding them into daily development and engineering workflows to improve productivity, automation, and system level efficiency across the SW department and overall R&D organization.