Summary
Daniel Pavlicek is a Lead ML Engineer based in Berlin with a decade of experience building production-grade AI systems across NLP, computer vision, and generative models. He has led end-to-end ML efforts from data acquisition to deployment—most recently delivering a production underwater stereo vision pipeline for biomass estimation and previously shipping an NLP-driven MVP as CTO/Lead Data Scientist at a startup. His background in mathematics (BSc Bristol, MSc La Sapienza) underpins a pragmatic approach to model calibration, stereo geometry and statistical rigor. Comfortable bridging research and product, he focuses on turning complex unstructured data into actionable insights and scaling models for real-world use. An engineer who blends hands-on implementation with leadership, he’s equally at home tuning model accuracy and orchestrating cross-functional teams to align AI with business outcomes.
10 years of coding experience
8 years of employment as a software developer
TU Berlin
Master's degree, Mathematics, Master's degree, Mathematics at Università degli Studi di Roma 'La Sapienza'
Bachelor of Science (B.Sc.), Mathematics, Bachelor of Science (B.Sc.), Mathematics at Bristol University
English, Italian, German