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]
WeAreIn for driving decarbonization and digitalization.
As a global leader in semiconductor solutions in power systems and IoT, Infineon enables game-changing solutions for green and efficient energy, clean and safe mobility, as well as smart and secure IoT. Together, we drive innovation and customer success, while caring for our people and empowering them to reach ambitious goals. Be a part of making life easier, safer and greener.
Are you in?
We are on a journey to create the best Infineon for everyone.
This means we embrace diversity and inclusion and welcome everyone for who they are. At Infineon, we offer a working environment characterized by trust, openness, respect and tolerance and are committed to give all applicants and employees equal opportunities. We base our recruiting decisions on the applicant´s experience and skills. Learn more about our various contact channels.
We look forward to receiving your resume, even if you do not entirely meet all the requirements of the job posting.
Please let your recruiter know if they need to pay special attention to something in order to enable your participation in the interview process.
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