Summary
Nicolas Thiebaut is an ML practitioner and educator with 11 years of experience applying deep learning and NLP to real-world product problems, currently splitting his time between senior ML engineering at Roblox and teaching MLOps and deep learning at the University of San Francisco and Université de Technologie de Troyes. He has led end-to-end ML systems in hiring and recruitment platforms—building low-latency real-time scoring, Transformer-based text classifiers with >95% accuracy, Siamese recommendation networks, and robust monitoring/alerting for production models. As an engineering manager at Hired he defined ML roadmaps, hiring processes, and spearheaded fair ML initiatives, bringing fairness metrics and mitigation into production pipelines. A former physicist with a PhD and rigorous quantitative background, he combines research sensibility (published papers and a patent on model feedback) with strong deployment experience (SageMaker, FastAPI, GitHub Actions). Hard-to-spot strengths: he repeatedly turns academic ideas into scalable features that automate large fractions of workflows (20–50% candidate automation) while keeping systems production-hardened.
11 years of coding experience
8 years of employment as a software developer
Master 2, Condensed Matter Physics, Master 2, Condensed Matter Physics at Ecole normale supérieure
Doctor of Philosophy (PhD), Theoretical Physics, Doctor of Philosophy (PhD), Theoretical Physics at Université Paris Sud (Paris XI)
Master’s Degree, Magistère de physique fondamentale d'Orsay, Master’s Degree, Magistère de physique fondamentale d'Orsay at Université Paris-Saclay