Metod Jazbec
Machine Learning Engineer
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolMetod Jazbec is a machine learning engineer and PhD student at the Amsterdam Machine Learning Lab with nine years of quantitative and research experience across industry and academia. He blends rigorous mathematics with practical ML, having built privacy-preserving gradient boosting at ETH Zürich and Bayesian HMMs and NLP sentiment pipelines for systematic trading. His industry research internships at Microsoft and Apple focused on data-efficient fine-tuning of LLMs and machine learning research, complementing production experience modernizing trading systems and deploying portfolio optimization at Move Digital. Metod’s background spans top programs in Data Science, Mathematics, and Financial Mathematics with top honors, reflecting strong theoretical grounding and applied skill. He operates comfortably at the intersection of probabilistic modeling, privacy-aware ML, and production engineering—often translating complex models into deployable systems. Outside obvious research credentials, he curates technical work publicly (metodj.github.io), signaling a commitment to reproducibility and clear communication.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at University of AmsterdamMaster's degree, Data Science, 5.75 / 6.0 (with distinction), Master's degree, Data Science, 5.75 / 6.0 (with distinction) at ETH ZürichBachelor's degree, Mathematics, Bachelor's degree, Mathematics at Humboldt University of BerlinBachelor's degree, Financial Mathematics, 9.65 / 10.0 (with distinction), Bachelor's degree, Financial Mathematics, 9.65 / 10.0 (with distinction) at University of Ljubljana, Faculty of Mathematics and Physics
English, German, Croatian, Slovenian
Github Skills (21)
deep-learning9
neural-network8
bert7
sentiment-analysis7
linearization6
hex5
bayesian4
tensorflow4
autoregressive-models3
diffusion-models2
pytorch2
visual-recognition2
nlp2
rust2
natural-language-processing2
Programming languages (4)
Github contributions (5)
metodj/Laplace
Sep 2021 - Feb 2023
Laplace approximations for Deep Learning.
Contributions:9 PRs, 49 pushes, 3 branches in 1 year 5 months
deep-learningmachine-learninglaplace
metodj/metodj.github.io
Sep 2022 - Sep 2022
Contributions:30 commits, 26 pushes in 6 days
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