Maxime Labonne is Head of Post-Training at Liquid AI, where he works on adapting and aligning large language models for real-world use. Post-training, the discipline of fine-tuning and shaping a model’s behavior after pretraining, is one of the most consequential stages in modern AI, and Maxime is widely regarded as one of its leading practitioners and educators.

His expertise is grounded in research: he holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and has been recognized as a Google Developer Expert in AI and ML. Beyond his day job, Maxime is a prolific open source contributor. He created the LLM Course, a structured learning path followed by a large community of practitioners, along with tutorials on fine-tuning and tools such as LLM AutoEval, and he has released state-of-the-art models like NeuralDaredevil that have pushed the boundaries of what open fine-tuning can achieve.

Maxime is also a best-selling author. He wrote LLM Engineer’s Handbook with Paul Iusztin, an end-to-end guide to building production LLM applications that covers the full pipeline from problem description to deployment, and Hands-On Graph Neural Networks Using Python, a practical introduction to deep learning on graphs. Through his books, tutorials, and open source work, he has become one of the most trusted guides for engineers entering the LLM field.