Karl Hajjar is a Machine Learning Engineer based in Paris with eight years of experience bridging rigorous applied mathematics and production ML. He holds a PhD from Université Paris-Saclay and has advanced research credentials—including a visiting PhD stay at EPFL and a Microsoft AI internship collaborating on extensions to the Tensor Program for sequential models. Karl has moved ideas from theory to impact across industry roles at Nabla, Zalando and InstaDeep, deploying probabilistic and attention-based systems and applying RL+MCTS to combinatorial optimization challenges. Comfortable both in deep theoretical work on wide neural networks and in shipping production systems, he combines formal training from École normale supérieure and École Polytechnique with hands-on engineering. Outside work he brings a creative perspective shaped by a passion for music, an uncommon thread that often informs his approach to modelling and signal-like data.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at Université Paris-Saclay
Master 2 (M2), Mathematics, Machine Learning, Computer Vision, Graduated with Highest Honours (Mention Très Bien), Master 2 (M2), Mathematics, Machine Learning, Computer Vision, Graduated with Highest Honours (Mention Très Bien) at École normale supérieure Paris-Saclay
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