Science & Research Jobs in Beijing, China (Now Hiring) — 562 open — Page 29

Your Role

We are looking for an Edge AI Software Engineer to join our team and help develop, deploy, and optimize AI/ML solutions for edge devices. In this role, you will work at the intersection of embedded systems, machine learning, and real-time software, enabling intelligent features on resource-constrained hardware.

The ideal candidate has hands-on experience with embedded AI deployment, understands real-time system constraints, and can collaborate effectively across firmware, hardware, and algorithm teams to bring production-ready Edge AI solutions to market.

Key responsibilities in your new role

• Design and deploy AI/ML models on embedded and edge devices such as MCUs and AI-enabled SoCs.
• Integrate AI inference pipelines into embedded software stacks and real-time operating system environments.
• Optimize models and runtime performance for latency, memory usage, power efficiency, and reliability.
• Develop and maintain data processing pipelines for feature extraction, buffering, and a synchronous inference.
• Work closely with cross-functional teams including firmware, hardware, and algorithm engineers to ensure successful system integration.
• Troubleshoot and resolve deployment issues related to timing, memory allocation, data flow, and runtime behavior.
• Support maintainable deployment processes through version control, reproducibility, and trace ability.
• Evaluate and apply appropriate tools and frameworks for model conversion, quantization, and on-device inference.

Your Profile

Qualifications and skills to help you succeed

• Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Embedded Systems, or a related field.
  • Strong programming skills in C/C++ for embedded software development.
  • Solid understanding of RTOS concepts, task scheduling, interrupts, DMA, synchronization, and real-time constraints.
  • Experience deploying AI/ML models to edge devices using frameworks or toolchains such as TensorFlow Lite Micro, ExecuTorch, CMSIS-NN, or similar.
  • Good understanding of model quantization, memory optimization and performance tuning on constrained hardware.
  • Familiarity with sensor or signal processing workflows, including time-series data such as vibration, audio, current, or power signals.
  • Strong debugging and problem-solving skills across software, model, and hardware integration layers.
  • Ability to support the full deployment lifecycle from prototype through production.
• Preferred Qualifications
  • Experience with MCU-based AI acceleration or related edge inference platforms.
  • Familiarity with Free RTOS, RT-Thread, or similar embedded operating systems.
  • Experience in IoT, industrial, or consumer electronics applications.
  • Knowledge of model lifecycle management, firmware release processes, and production validation.
  • Experience using Python-based ML tools for model preparation, evaluation, and conversion.

Contact:
    [email protected]

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