Machine Learning Engineer - Senior

Full-time

Zurich

Posted 2 days ago

Develop 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.

Cyberwave'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.

Our 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.

We 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.

We 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.

PythonC++Py Torch Tensor Flow JAXDeep Learning Reinforcement Learning

Sim2real Transfer Mu Jo Co Isaac  Sim Domain Randomization Control Theory Computer VisionSLAM

• Degree in Computer Science, Robotics, AI, or a related field (BSc/MSc/PhD), with a strong foundation in applied mathematics, control theory, or computational modeling
• 3+ years of hands-on experience developing and deploying machine learning and deep learning models using frameworks such as PyTorch, Tensor Flow, or JAX
• Demonstrated expertise in reinforcement learning, including implementation of algorithms like PPO, SAC, or DDPG in both simulated and real-world environments
• Deep understanding of sim-to-real techniques, including domain randomization, domain adaptation, transfer learning, and policy robustness across environments
• Practical experience with physics-based simulators (e.g., Mu Jo Co , Isaac Sim, PyBullet) and hands-on work with robotic hardware or embedded platforms
• Fluent in Python, with strong software engineering practices; working knowledge of C++ is essential for performance-critical systems
• Strong grasp of data-driven modeling, system identification, control strategies, and optimization methods relevant to robotic learning and deployment

• Develop deep learning and reinforcement learning policies for perception, control, and decision-making (e.g., visuomotor policies, MPC-guided RL, goal-conditioned policies)
• Design and optimize cutting-edge ML/DL models for real-world robotics, tackling high-dimensional, dynamic, and noisy environments
• Develop advanced reinforcement learning agents in simulated environments such as Mu Jo Co , Isaac Lab/Sim, PyBullet, or proprietary simulators—pushing the boundaries of what machines can learn
• Lead sim2real transfer efforts, leveraging domain randomization, adaptation, and robust policy learning to ensure models generalize from virtual to physical systems
• Deploy end-to-end ML pipelines integrated with robotics or embedded systems, enabling real-time perception, decision-making, and control
• Collaborate across disciplines—working closely with simulation, hardware, and software teams to solve complex, system-level challenges
• Drive rapid experimentation, analyzing results, debugging performance bottlenecks, and continuously refining models for optimal real-world performance
• Build robust and scalable ML infrastructure, supporting automated training, evaluation, and deployment workflows across diverse robotic platforms

Work on cutting-edge ML and robotics challenges that translate directly into real-world impact across industries and society

Join a world-class, cross-disciplinary team that values innovation, curiosity, and bold thinking

Competitive compensation, including a strong salary package and meaningful equity options

Flexible work culture with support for remote work and autonomy over your schedule - outcomes over hours

Access to state-of-the-art simulation environments and robotic systems, from digital twins to physical platforms