人工智能框架软件工程师 – 模型压缩算法

Responsibilities include: ● Develop Intel Neural Compressor product and related tools (auto-round), optimize for Intel AI platform, including CPU, GPU and AI Accelerator ● Research and implement quantization and compression techniques for large language models (LLMs) and text-to-image/video generation models ● Track and explore cutting-edge directions in efficient model deployment and inference/finetuning acceleration. Qualifications: ● Bachelor’s or master’s degree, major in computer science or related subjects ● Solid understanding of deep learning, deep learning framework and large language model (LLM) fundamentals ● Familiarity with model compression techniques such as quantization and pruning ● Proficiency in Python/C++ or other programming languages commonly used for deep learning development ● Strong sense of teamwork and group collaboration ● Good English oral and written skill Preferred Qualifications ● Strong self-motivation and problem-solving skills ● Passion for technological innovation and practical engineering, with a drive for continuous exploration and improvement ● Experience in model fine-tuning, inference optimization or related tool development is a plus

Responsibilities include: • Develop Intel Neural Compressor product and related tools (auto-round), optimize for Intel AI platform, including CPU, GPU and AI Accelerator • Research and implement quantization and compression techniques for large language models (LLMs) and text-to-image/video generation models • Track and explore cutting-edge directions in efficient model deployment and inference/finetuning acceleration. Qualifications: • Bachelor’s or master’s degree, major in computer science or related subjects • Solid understanding of deep learning, deep learning framework and large language model (LLM) fundamentals • Familiarity with model compression techniques such as quantization and pruning • Proficiency in Python/C++ or other programming languages commonly used for deep learning development • Strong sense of teamwork and group collaboration • Good English oral and written skill Preferred Qualifications • Strong self-motivation and problem-solving skills • Passion for technological innovation and practical engineering, with a drive for continuous exploration and improvement • Experience in model fine-tuning, inference optimization or related tool development is a plus