Staff Machine Learning Engineer

As a Qualcomm Software Engineer, you will be at the forefront of designing, developing, modifying, and validating embedded and cloud edge software, applications, and specialized utility programs. Your primary focus will be on launching cutting-edge products that exceed customer expectations. Collaborating with various teams, you will contribute to the design of system-level software solutions and gather performance requirements and interfaces information. Responsibilities: - Spearhead the development and commercialization of the Qualcomm AI Runtime (QAIRT) SDK on Qualcomm SoCs. - Push the boundaries of performance from large Generative AI models. - Deploy large C/C++ software stacks using best practices. - Stay updated on the latest GenAI advancements, focusing on LLMs/Transformers and edge-based GenAI deployment. - Demonstrate a strong passion for the role of edge in AI's evolution. Requirements: - Masters/Bachelors degree in computer science or equivalent. - 6+ years of relevant work experience in software development. - Strong understanding of Generative AI models such as LLM, LVM, LMMs, and building blocks like self-attention, cross attention, kv caching, etc. - Familiarity with floating-point, fixed-point representations, and quantization concepts. - Experience optimizing algorithms for AI hardware accelerators (CPU/GPU/NPU). - Proficiency in C/C++ programming, Design Patterns, and OS concepts. - Proficient in Python scripting. - Excellent analytical and debugging skills. - Strong communication skills (verbal, presentation, written). - Ability to collaborate effectively across diverse global teams and interests. Preferred Qualifications: - Strong understanding of SIMD processor architecture and system design. - Proficiency in object-oriented software development and familiarity with Linux and Windows environments. - Background in kernel development for SIMD architectures. - Familiarity with frameworks like llama.cpp, MLX, and MLC is a plus. - Good knowledge of PyTorch, TFLite, and ONNX Runtime is preferred. - Experience with parallel computing systems and languages like OpenCL and CUDA is advantageous. As a Qualcomm Software Engineer, you will be at the forefront of designing, developing, modifying, and validating embedded and cloud edge software, applications, and specialized utility programs. Your primary focus will be on launching cutting-edge products that exceed customer expectations. Collaborating with various teams, you will contribute to the design of system-level software solutions and gather performance requirements and interfaces information. Responsibilities: - Spearhead the development and commercialization of the Qualcomm AI Runtime (QAIRT) SDK on Qualcomm SoCs. - Push the boundaries of performance from large Generative AI models. - Deploy large C/C++ software stacks using best practices. - Stay updated on the latest GenAI advancements, focusing on LLMs/Transformers and edge-based GenAI deployment. - Demonstrate a strong passion for the role of edge in AI's evolution. Requirements: - Masters/Bachelors degree in computer science or equivalent. - 6+ years of relevant work experience in software development. - Strong understanding of Generative AI models such as LLM, LVM, LMMs, and building blocks like self-attention, cross attention, kv caching, etc. - Familiarity with floating-point, fixed-point representations, and quantization concepts. - Experience optimizing algorithms for AI hardware accelerators (CPU/GPU/NPU). - Proficiency in C/C++ programming, Design Patterns, and OS concepts. - Proficient in Python scripting. - Excellent analytical and debugging skills. - Strong communication skills (verbal, presentation, written). - Ability to collaborate effectively across diverse global teams and interests. Preferred Qualifications: - Strong understanding of SIMD processor architecture and system design. - Proficiency in object-oriented software development and familiarity with Linux and Windows environments. - Background in kernel development for SIMD architectures. - Familiarity with frameworks like llama.cpp, MLX, and MLC is a plus. - Good knowledge of PyTorch, TFLite, and ONNX Runtime is preferred. - Experience with parallel computing systems and languages like OpenCL and CUDA is advantageous.