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We are seeking a Research Intern to explore innovative approaches for building efficient multimodal language models. The role will focus on techniques such as model compression, quantization, and model optimization for efficient deployment on resource-constrained platforms. You will work on training strategies to enhance performance and scalability across vision-language tasks.
Job Responsibility:
Prototype implementations
designing experiments
analyzing results
contributing to research that pushes the boundaries of efficiency in AI systems
Requirements:
Accepted or currently enrolled in a PhD program in Computer Science or related STEM field
Foundation in machine learning and deep learning, with expertise in areas such as multimodal language models, transformer architecture, efficient model design, compression, and quantization
Proficiency in modern deep learning frameworks (e.g., PyTorch, DeepSpeed) for scalable model development and optimization
Proven ability to define and execute original research agendas, demonstrating creativity and technical rigor
Motivation to publish in top-tier academic venues, showcasing impactful contributions to the research community.
Nice to have:
Familiarity with multimodal architectures and low-bit quantization