Nikita holds a PhD from ETH Zurich in deep reinforcement learning for robotics, where his research - including the widely cited paper "Learning to Walk in Minutes" - showed that massively parallel training could produce legged locomotion policies orders of magnitude faster than existing methods, reshaping how the field approaches sim-to-real transfer. He spent several years afterward as an industry research scientist, applying that same approach to real robots at a time when almost no one else was pursuing it. As CEO, he sets Flexion's strategic direction and stays close to the reinforcement learning research behind the company’s mission.

David holds a PhD from ETH Zurich in reinforcement learning and spent over a decade on robot learning and simulation, most notably as a research manager at NVIDIA, where he led development of Isaac Gym and Isaac Lab, the simulation infrastructure much of the robotics industry now trains on. As CTO, he sets Flexion's overall technical direction, translating the strategic vision into concrete technical execution, creating a coherent partnership between strategic ambition and engineering results.

Julian holds a PhD from the Max Planck Institute, his research tackles one of robotics' hardest unsolved problems: knowing precisely where a robot is and what surrounds it outside controlled environments. That work gave him mastery of the full perception stack that makes autonomous navigation possible at scale. As Perception lead, he is turning that expertise into the layer that lets every other system trust what the robot sees.

Fabian's background is rooted in ETH Zurich's Robotic Systems Lab, where he focused on actuator design, energy efficiency, and the experimental validation that turns simulation results into hardware that holds up under real conditions. That work included overseeing the mechanical, electrical, and software development of more than ten robotic platforms end to end. As Hardware Lead, he is making sure the "real" side of a sim-to-real company holds up across multiple morphologies and deployment conditions.