Machine Learning Engineer, VLA & RL - Senior

ph3Overview /h3 pMachine Learning Engineer, VLA RL - Senior. Full-time. TC starting at 70k. Posted 1 day ago. /p pBuild vision-language-action and reinforcement learning models for real-world robotic systems. Train policies that generalize across embodiments, tasks, simulators, and physical deployments. /p h3About Cyberwave /h3 pCyberwave 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. /p h3Role /h3 pWe\'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. /p pThis 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. /p pThis role is based in Milan or Zurich, with regular access to real robots, simulation infrastructure, and customer-facing deployment scenarios. /p h3Work Style /h3 pHands-on applied ML for embodied AI, simulation, and real robot deployments /p h3Requirements /h3 ul li3+ years of hands-on experience building ML systems for robotics, embodied AI, reinforcement learning, or visuomotor control /li liSpecific experience with vision-language-action (VLA) models, robotic foundation models, imitation learning, behavior cloning, or language-conditioned policies /li liStrong experience with reinforcement learning algorithms and workflows, such as PPO, SAC, offline RL, RL fine-tuning, reward modeling, or policy evaluation /li liPractical experience with cross-embodiment transfer, including transferring policies across robot morphologies, sensors, action spaces, simulators, or real hardware platforms /li liExperience training and evaluating policies in simulation environments such as MuJoCo, Isaac Sim/Lab, PyBullet, ManiSkill, robosuite, or similar robotics simulators /li liStrong Python and PyTorch skills, with good software engineering habits for reproducible training, experiment tracking, datasets, and evaluation /li liComfort debugging model failures across perception, action representations, control loops, latency, data quality, and hardware behavior /li liComfortable working in English in an international, fast-moving environment /li /ul h3Responsibilities /h3 ul liTrain and evaluate VLA, imitation learning, and reinforcement learning policies for real robotic tasks /li liBuild model and data pipelines for language-conditioned robot control, visuomotor policies, trajectory datasets, and action representations /li liDesign experiments for cross-embodiment transfer across arms, mobile robots, drones, simulated systems, and physical hardware /li liImprove policy robustness through simulation, domain randomization, dataset curation, offline evaluation, online rollouts, and sim-to-real validation /li liCollaborate with robotics and infrastructure teams to deploy learned policies into Cyberwave\'s edge, simulation, and digital twin stack /li liCreate rigorous evaluation suites for task success, generalization, safety, latency, and real-world reliability /li liStay close to frontier research in embodied AI while turning useful ideas into production-quality systems /li /ul h3What We Offer /h3 pWork on frontier embodied AI with direct paths to real robot deployment /p pAccess to real robots, simulation infrastructure, and robotics datasets /p pCompetitive compensation and meaningful equity /p pJoin a high-talent team of repeat founders, ex-Google engineers, and PhDs /p pIn-person collaboration in Milan and Zurich with real hardware and high ownership /p h3Ready to Join Our Team? /h3 pWe\'d love to hear from you! When applying, please include: /p ul liYour Github or LinkedIn profile /li li2-3 lines about why you would like to join Cyberwave /li /ul pTell us what excites you about this opportunity and how you can contribute to our mission! /p /p #J-18808-Ljbffr