Software Developer, Embedded AI/ML Platform

We are seeking a motivated Junior Software Developer to join our dynamic team focused on developing the low-level software platform for next-generation in-vehicle embedded devices. The ideal candidate will have a strong passion for embedded systems, C++ development, and a keen interest in the practical deployment of AI models on specialized hardware. You will work closely with senior developers, AI/ML engineers and data scientists to design, implement, and optimize robust and efficient low-level software solutions that unlock the full potential of our in-vehicle AI capabilities. Key Responsibilities: Develop, implement, and maintain the low-level software platform modules for in-vehicle embedded devices, specifically targeting NPU and other accelerator integration. Design and implement software for efficient deployment, loading, and execution of AI/ML models (including GenAI) on embedded hardware accelerators. Optimize models for real-time performance, memory footprint, and power consumption on constrained embedded systems. Collaborate with cross-functional teams to gather requirements and translate them into technical specifications. Write clean, efficient, and well-documented code following best practices. Participate in code reviews and contribute to team knowledge sharing. Debug and troubleshoot issues in existing applications. Stay updated with the latest industry trends and technologies related to AI/ML and C++ standards for embedded systems. Qualifications: Bachelor s degree or similar in Computer Science, Software Engineering, or a related field. Minimum of 2+ years of professional experience in C/C++ programming, including with embedded systems development. Proven experience with low-level programming and direct hardware interaction. Strong problem-solving skills and attention to detail. Ability to work collaboratively in a team environment. Excellent communication skills, both verbal and written. Preferred Skills: Understanding of AI/ML concepts and exposure to AI frameworks (e.g., LiteRT, ONNX Runtime, Executorch, or similar embedded inference engines). Experience with version control systems (e.g., Git). Experience in deep learning. Experience optimizing and deploying ML models on embedded devices.