Leonie grew up on her grandparents’ farm in southern Germany, where early mornings were simply how the day began.

Before Dryft, she's already been building software for 10+ years: As a sports car engineer at Porsche, she engineered the Taycan’s predictive drivetrain controller (now patented), leading to her research at the University of Waterloo, where she developed Model Predictive Control methods to improve vehicle handling and stability. Later, she worked as an ML engineer in Stanford and did research in time-series forecasting, co-developing NeuralProphet (>3M downloads), the model evaluation framework "Test-of-time" and the HPDNorm framework. Along the way she also led TUM.ai, a 100-person applied-AI team, which is Europe’s largest AI student initiative.

She completed her master’s in ML & Robotics @TUM after studying automotive engineering as an undergraduate, where she graduated valedictorian.

Leonie loves to do hackathons (won at Treehacks) and went a full year without skipping a single day of workout.