Machine Learning Engineer, VLA
Pubblicato il 30-08-2026 - Altro in Italia
Overview
Machine Learning Engineer, VLA & RL - Senior. Full-time. TC starting at 70k. Posted 1 day ago.
Build vision-language-action and reinforcement learning models for real-world robotic systems. Train policies that generalize across embodiments, tasks, simulators, and physical deployments.
About Cyberwave
Cyberwave is building the infrastructure layer for intelligent machines - making robotics as accessible, scalable, and programmable as cloud software. Our platform connects simulation, digital twins, edge devices, cloud training, and real robots into one operating layer for robotics teams.
Role
We're looking for a Machine Learning Engineer focused on vision-language-action (VLA) models, reinforcement learning, and cross-embodiment transfer. You'll work on models that turn perception, language, and task context into robot actions across different hardware platforms: arms, mobile robots, drones, and other industrial systems.
This is a hands-on applied ML role. We care about candidates who have trained and evaluated real policies,
debugged failures across simulation and hardware, and understand the gap between promising demos and reliable deployment. You'll work closely with robotics, simulation, infrastructure, and product teams to build learning systems that can be trained at scale, evaluated rigorously, and deployed safely on real robots.
This role is based in Milan or Zurich, with regular access to real robots, simulation infrastructure, and customer-facing deployment scenarios.
Work Style
Hands-on applied ML for embodied AI, simulation, and real robot deployments
Requirements
3+ years of hands-on experience building ML systems for robotics, embodied AI, reinforcement learning, or visuomotor control
Specific experience with vision-language-action (VLA) models, robotic foundation models, imitation learning, behavior cloning, or language-conditioned policies
Strong experience with reinforcement learning algorithms and workflows, such as PPO, SAC, offline RL,