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.

Senior Software Developer, Embedded AI/ML Platform