Staff Embedded Machine Learning Engineer

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Strong hands-on experience in performance optimization for embedded or low-power systems. Excellent in C++ programming, with a focus on system-level and runtime development. Solid understanding of embedded system design, including memory hierarchy and hardware-software interaction. Experience with Linux/Android/QNX development environments and toolchains. Familiarity with computer architecture, especially for AI accelerators or DSPs. Solid knowledge of machine learning concepts and model structures. Optimization of algebraic operations in algorithms for HW cores. Knowledge on deep learning and popular frameworks is an asset. Design and implement core components of the ML runtime framework for inference on embedded systems. Collaborate with compiler, hardware, and model teams to co-design efficient execution paths for AI workloads. Develop and maintain C++ code for runtime kernels and system-level integration. Develop tools to assist with performance profiling and debugging of quantized model accuracy Analyze and improve runtime behavior using profiling tools and hardware counters. Challenging the status quo and driving innovations to be the best-of-class.