Skander Moalla is a final-year PhD candidate in reinforcement learning and LLM post-training at EPFL with seven years of research and engineering experience across academia and industry, currently interning at Meta FAIR. He specializes in scaling RL for large language models, developing off-policy and offline fine-tuning algorithms (e.g., Quantile Reward Policy Optimization) and exposing links between plasticity, trust regions, and off-policy collapse. Skander has co-led post-training of a fully open-source 70B LLM (Apertus 70B), contributed production-grade JAX code at DeepMind, and built scalable pipelines and sandboxes for reproducible ML experiments. Comfortable shipping infrastructure and research code from scratch, he combines rigorous theoretical work with hands-on systems development and a track record of award-winning reproducibility studies. Open to full-time roles in Zurich, Paris, or London for Fall 2026, he brings a rare blend of RL theory, LLM post-training expertise, and practical engineering at scale.
7 years of coding experience
1 year of employment as a software developer
Master of Science - MSc, Advanced Computer Science, Master of Science - MSc, Advanced Computer Science at University of Oxford
Exchange program - Fall semester, Mathematics and Computer Science, Exchange program - Fall semester, Mathematics and Computer Science at University of Toronto
Baccalauréat, Mathématiques, Baccalauréat, Mathématiques at Lycée Pilote Bourguiba de Tunis
Bachelor of Science - BS, Mathematics and Computer Science, Bachelor of Science - BS, Mathematics and Computer Science at École Polytechnique
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at EPFL
International Honors Program (IHP) - Summer quarter, Technology and Innovation Intensive studies, International Honors Program (IHP) - Summer quarter, Technology and Innovation Intensive studies at Stanford University
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Skander Moalla - Research Scientist Intern at EPFL IC