Yonk Shi is an AI engineer and former PhD researcher who blends deep representation-learning expertise with 11 years of software engineering experience to deliver production-ready ML systems. He has published at NeurIPS and ICLR and has built end-to-end solutions across computer vision, speech, and language—shipping segmentation models, audio classifiers, and multilingual transformer evaluations. At companies from startups to industrial partners in Sweden, he’s led teams, architected GCP Vertex AI pipelines, and deployed models to edge devices while keeping reliability and human-in-the-loop quality workflows central. Recently he helped scale Kive.ai’s studio-grade image/video generation quality engine and co-founded a voice-based language tutor that combines ASR with LLM orchestration. Colleagues describe him as curious, dependable, and pragmatic—someone who turns open-ended research ideas into testable, production-grade features. Outside work he’s an avid sailor and amateur MSG evangelist, hinting at a practical, experimental approach to both cooking and engineering.
11 years of coding experience
6 years of employment as a software developer
University of California, San Diego
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at KTH Royal Institute of Technology
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