22 set - Milano
Cyberwave
OverviewTutti i candidati sono invitati a leggere attentamente la seguente descrizione del lavoro e le relative informazioni prima di candidarsi.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 CyberwaveCyberwave 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.RoleWe '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 StyleHands-on applied ML for embodied AI, simulation, and real robot deploymentsRequirements3+ years of hands-on experience building ML systems for robotics, embodied AI, reinforcement learning, or visuomotor controlSpecific experience with vision-language-action (VLA) models, robotic foundation models, imitation learning, behavior cloning, or language-conditioned policiesStrong experience with reinforcement learning algorithms and workflows, such as PPO, SAC, offline RL, RL fine-tuning, reward modeling, or policy evaluationPractical experience with cross-embodiment transfer, including transferring policies across robot morphologies, sensors, action spaces, simulators, or real hardware platformsExperience training and evaluating policies in simulation environments such as MuJoCo, Isaac Sim/Lab, PyBullet, ManiSkill, robosuite, or similar robotics simulatorsStrong Python and PyTorch skills, with good software engineering habits for reproducible training, experiment tracking, datasets, and evaluationComfort debugging model failures across perception, action representations, control loops, latency, data quality, and hardware behaviorComfortable working in English in an international,
fast-moving environmentResponsibilitiesTrain and evaluate VLA, imitation learning, and reinforcement learning policies for real robotic tasksBuild model and data pipelines for language-conditioned robot control, visuomotor policies, trajectory datasets, and action representationsDesign experiments for cross-embodiment transfer across arms, mobile robots, drones, simulated systems, and physical hardwareImprove policy robustness through simulation, domain randomization, dataset curation, offline evaluation, online rollouts, and sim-to-real validationCollaborate with robotics and infrastructure teams to deploy learned policies into Cyberwave 's edge, simulation, and digital twin stackCreate rigorous evaluation suites for task success, generalization, safety, latency, and real-world reliabilityStay close to frontier research in embodied AI while turning useful ideas into production-quality systemsWhat We OfferWork on frontier embodied AI with direct paths to real robot deploymentAccess to real robots, simulation infrastructure, and robotics datasetsCompetitive compensation and meaningful equityJoin a high-talent team of repeat founders, ex-Google engineers, and PhDsIn-person collaboration in Milan and Zurich with real hardware and high ownershipReady to Join Our Team? xysqume We 'd love to hear from you! When applying, please include:Your Github or LinkedIn profile2-3 lines about why you would like to join CyberwaveTell us what excites you about this opportunity and how you can contribute to our mission!#J-18808-Ljbffr
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