Embedded Systems Engineer with Deep Learning Expertise

Years of experience- 2+ Salaly $70,000-$90,000

Position Overview:

As an Embedded Systems Engineer, you will work at the intersection of AI and hardware, bringing deep learning models to life on embedded platforms. You will collaborate closely with AI researchers, data scientists, and hardware engineers to optimize and deploy machine learning algorithms on resource-constrained devices, ensuring performance, accuracy, and energy efficiency. Your role will have a direct impact on the next generation of AI products.

Key Responsibilities:

• Design, develop, and optimize embedded systems for deep learning applications, ensuring real-time performance on edge devices.
• Integrate deep learning models into embedded platforms such as microcontrollers, DSPs, FPGAs, and GPUs.
• Collaborate with AI researchers to quantize, compress, and optimize neural networks for deployment on low-power devices.
• Prototype and implement deep learning solutions for embedded systems in areas like computer vision, sensor fusion, and speech recognition.
• Conduct performance benchmarking and testing of deep learning algorithms on various embedded hardware platforms.
• Debug and troubleshoot hardware-software integration issues, ensuring smooth communication between AI algorithms and embedded hardware.
• Stay up-to-date with the latest developments in embedded systems, AI, and edge computing technologies.

Required Qualifications:

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• 3+ years of experience in embedded systems development.
• Proficiency in embedded C/C++ programming and experience with RTOS or Linux-based embedded systems.
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or ONNX.
• Solid understanding of hardware acceleration for deep learning using tools like TensorRT, OpenCL, or CUDA.
• Familiarity with model optimization techniques (e.g., quantization, pruning, and compression).
• Strong knowledge of communication protocols (SPI, I2C, UART, etc.) and hardware interfaces.
• Experience with ARM-based processors or other embedded architectures.
• Problem-solving mindset with the ability to work in a fast-paced, collaborative environment.