cyberwave digital agency

ppFull-time /ppZurich /ppPosted 2 days ago /ppDevelop and deploy machine learning models, with a focus on deep learning, reinforcement learning, and simulation-to-reality (sim2real) transfer for real-world robotics and control systems. /ppCyberwave's vision is to unlock the full potential of intelligent machines by making robotics as accessible, scalable, and programmable as cloud software. We believe in a future where deploying robotic systems is no longer limited by complexity, fragmentation, or vendor lock-in. /ppOur mission is to accelerate this future through an AI-powered robotics platform that abstracts away hardware complexity and empowers developers to build, deploy, and scale robotic applications with high-level, intuitive commands.

By bridging classical robotics frameworks (like ROS and ROS 2) with modern machine learning in a modular architecture, Cyberwave simplifies integration across heterogeneous systems. With built-in web-based simulation and digital twin tools, we enable seamless development, real-time monitoring, and faster iteration from concept to deployment—both in simulation and the real world. /ppWe are building an A+ team with talent based in Zurich, Milan, and Rome. Join our dynamic and collaborative environment—whether from our Zurich headquarters or remotely within a similar time zone. Enjoy the flexibility to shape your own schedule while staying aligned with our shared goals and fast-paced mission. /ppWe are seeking a highly skilled and motivated Machine Learning Engineer to join our AI team.

This role involves developing and deploying machine learning models, with a focus on deep learning, reinforcement learning, and simulation-to-reality (sim2real) transfer for real-world robotics and control systems.

You'll work closely with software, robotics, and hardware teams to build intelligent systems that learn in simulation and perform in the real world. /ppPythonC++PyTorchTensorFlowJAXDeep LearningReinforcement LearningSim2real TransferMuJoCoIsaac SimDomain RandomizationControl TheoryComputer VisionSLAM /ph3Requirements /h3ulliDegree in Computer Science, Robotics, AI, or a related field (BSc/MSc/PhD), with a strong foundation in applied mathematics, control theory, or computational modeling /lili3+ years of hands-on experience developing and deploying machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, or JAX /liliDemonstrated expertise in reinforcement learning, including implementation of algorithms like PPO, SAC, or DDPG in both simulated and real-world environments /liliDeep understanding of sim-to-real techniques, including domain randomization,

domain adaptation, transfer learning, and policy robustness across environments /liliPractical experience with physics-based simulators (e.g., MuJoCo, Isaac Sim, PyBullet) and hands-on work with robotic hardware or embedded platforms /liliFluent in Python, with strong software engineering practices; working knowledge of C++ is essential for performance-critical systems /liliStrong grasp of data-driven modeling, system identification, control strategies, and optimization methods relevant to robotic learning and deployment /li /ulh3Responsibilities /h3ulliDevelop deep learning and reinforcement learning policies for perception, control, and decision-making (e.g., visuomotor policies, MPC-guided RL, goal-conditioned policies) /liliDesign and optimize cutting-edge ML/DL models for real-world robotics, tackling high-dimensional, dynamic, and noisy environments /liliDevelop advanced

reinforcement learning agents in simulated environments such as MuJoCo, Isaac Lab/Sim, PyBullet, or proprietary simulators—pushing the boundaries of what machines can learn /liliLead sim2real transfer efforts, leveraging domain randomization, adaptation, and robust policy learning to ensure models generalize from virtual to physical systems /liliDeploy end-to-end ML pipelines integrated with robotics or embedded systems, enabling real-time perception, decision-making, and control /liliCollaborate across disciplines—working closely with simulation, hardware, and software teams to solve complex, system-level challenges /liliDrive rapid experimentation, analyzing results, debugging performance bottlenecks, and continuously refining models for optimal real-world performance /liliBuild robust and scalable ML infrastructure, supporting automated training, evaluation, and deployment workflows

across diverse robotic platforms /li /ulh3What We Offer /h3pWork on cutting-edge ML and robotics challenges that translate directly into real-world impact across industries and society /ppJoin a world-class, cross-disciplinary team that values innovation, curiosity, and bold thinking /ppCompetitive compensation, including a strong salary package and meaningful equity options /ppFlexible work culture with support for remote work and autonomy over your schedule - outcomes over hours /ppAccess to state-of-the-art simulation environments and robotic systems, from digital twins to physical platforms /p /p