Senior Software Developer, Embedded AI/ML Platform

Key Responsibilities:

• Lead the development and implementation of 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.
• Write clean, efficient, and well-documented code following best practices.
• Collaborate with cross-functional teams to define project requirements and deliver solutions that meet business needs.
• Lead code reviews, establish coding standards, and champion best practices for embedded C++ development.
• Stay abreast of industry trends and advancements in AI/ML technologies and C++ standards for embedded systems.

Qualifications:

• Bachelor s or Master s degree in Computer Science, Software Engineering, or a related field.
• 5+ years of experience in C++ development, with an emphasis on embedded systems.
• Experience in automotive/embedded C++.
• Demonstrable experience with low-level programming, direct hardware interaction, and device driver development.
• Extensive knowledge of AI/ML concepts, with hands-on experience in deploying and optimizing models using embedded inference frameworks (e.g., LiteRT, ONNX Runtime, Executorch or similar).
• Proven experience in software architecture and design patterns.
• Strong understanding of performance optimization techniques and memory management in C++.
• Excellent problem-solving skills, critical thinking, and the ability to troubleshoot complex issues in highly integrated systems.
• Strong communication, collaboration, and leadership skills, with the ability to mentor and guide junior developers.

Preferred Skills:

• Direct experience with the deployment and optimization of Large Language Models (LLMs) or similar generative AI models on edge devices.
• Familiarity with cloud platforms (e.g., AWS, Azure) and containerization technologies (e.g., Docker).
• Knowledge of additional programming languages (e.g., Python, Java).
• Experience with version control systems (e.g., Git) and CI/CD pipelines.