Engineer, Staff/Manager- Machine Learning/Machine Learning/Tools-C++/Python/Quantization

As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world-class products that meet and exceed customer needs. You will collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Responsibilities: - Spearhead the development and commercialization of the Qualcomm AI Runtime (QAIRT) SDK on Qualcomm SoCs. - Push the limits of performance from large models as an AI inferencing expert. - Deploy large C/C++ software stacks using best practices. - Stay updated on GenAI advancements, understanding LLMs/Transformers, and nuances of edge-based GenAI deployment. - Utilize power-efficient hardware and software stack to run Large Language Models (LLMs) and Large Vision Models (LVM) at near GPU speeds. Qualifications Required: - Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience, or Master's degree with 3+ years, or PhD with 2+ years. - 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc. - Masters/Bachelors degree in computer science or equivalent. - 14+ years of relevant work experience in software development. - Strong understanding of Generative AI models LLM, LVM, LMMs, and building blocks. - Experience with optimizing algorithms for AI hardware accelerators and strong in C/C++ programming. - Good scripting skills in Python and excellent analytical and debugging skills. - Good communication skills and ability to collaborate across a globally diverse team. - Strong understanding of SIMD processor architecture and system design is a plus. - Proficiency in object-oriented software development, familiarity with Linux and Windows environment, and background in kernel development for SIMD architectures. - Familiarity with frameworks like llama.cpp, MLX, and MLC, and knowledge of PyTorch, TFLite, and ONNX Runtime is preferred. - Experience with parallel computing systems and languages like OpenCL and CUDA is a plus.,