About this role
<p> </p> <p><strong>Your job:</strong></p> <p>• Develop, optimize, and deploy machine learning models for embedded and edge devices<br>• Ensure real-time performance, memory efficiency, and low-power operation of on-device AI solutions<br>• Integrate ML algorithms with microcontrollers, embedded controllers, and edge computing platforms<br>• Implement pipelines for model conversion, quantization, and hardware acceleration<br>• Collaborate with software, controls, and hardware engineers to deliver production-grade embedded AI systems<br>• Participate in prototyping, testing, benchmarking, and continuous improvement of embedded AI solutions<br>• Work with the global research teams to translate concepts into scalable prototypes for industrial automation</p> <p> </p> <p><strong>Your qualification:</strong></p> <ul> <li>Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Electronics, or related field</li> <li>2–5 years of hands-on experience in Embedded Systems and AI/ML development</li> <li>Strong programming proficiency in Python, C/C++, and embedded development</li> <li>Practical experiencewith embedded ML frameworks such as TensorFlow Lite, ONNX Runtime, Edge Impulse, or similar</li> <li>Solid understanding of real-time embedded systems, microcontroller architectures, and communication protocols</li> <li>Experience with model optimization techniques (quantization, pruning, hardware-specific acceleration) is a plus</li> <li>Familiarity with edge hardware platforms such as ARM Cortex, STM32, ESP32, NVIDIA Jetson, or similar is desirable</li> <li>Ability to work in a fast-paced research-driven environment with strong ownership and autonomy</li> </ul>