Jiaqi Zeng is a Senior Applied Scientist at NVIDIA with 7 years of experience building and productionizing efficient deep learning solutions for large language models and graph-based tasks. Trained at Carnegie Mellon, Jiaqi has practical expertise in parameter-efficient training, model alignment, and distributed training across hundreds of GPUs, having reduced storage costs by 99.9% for 3B-parameter models and productionized prompt- and p-tuning for thousands of conversational AI users. Her research background spans contrastive self-supervised learning, continual graph learning, and causal approaches for information extraction, with publications at AAAI and EMNLP and a hand in creating a 1.1M-conversation medical dialogue dataset. Based in California, she blends strong engineering rigor—CI/CD, Docker, Slurm, DDP, Apex—with academic depth to move cutting-edge ML methods into production-scale systems.
6 years of coding experience
2 years of employment as a software developer
Master's degree, Machine learning, Master's degree, Machine learning at Carnegie Mellon University School of Computer Science
Bachelor of Engineering - BE, Computer Science and Technology, Bachelor of Engineering - BE, Computer Science and Technology at Shanghai Jiao Tong University
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