Physical AI Engineer - Senior
Full-timeDevelop and deploy robots to perceive, learn, and adapt, bridging the gap between simulation and reality. This is a hands-on, engineering-first role that combines software architecture, real-time systems, and machine learning integration. You’ll design and optimize C++ systems that handle large-scale data exchange between digital twins and physical platforms, ensuring speed, precision, and reliability.
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.
Requirements
- Degree in Robotics, Computer Science, Electrical Engineering, Physics, or a related field (BSc/MSc/PhD), with a strong foundation in applied control engineering, computational modeling, mathematics, or physics
- 5+ years of experience developing production-grade software in C++ , with a focus on performance, memory management, and maintainability
- Hands-on experience with real robots including setup, customization, and integration with other systems
- Deep familiarity with robotics middleware, such as ROS or ROS 2
- Practical experience with physics-based simulators (e.g., MuJoCo, Isaac Sim, Newton, Gazebo) and hands-on work with robotic hardware or embedded platforms
- Deep familiarity with a range of message queuing protocols, data distribution services and serialization methods (e.g., MQTT, gRPC, DDS, HDF5, point clouds, and image stacks)
- Strong understanding of real-time systems, concurrency, and multi-threaded programming
- Experience with version control (Git), containerization and build systems (e.g., Docker, Bazel, CMake)
- Strong analytical and problem-solving abilities, excellent teamwork and collaboration skills
- Prior experience in startups, R&D labs, or environments requiring rapid prototyping and deployment
- Background in numerical methods, computational geometry, or game engine physics is a strong plus
Responsibilities
- Architect and optimize C++ core systems supporting real-time interaction between simulated and physical environments
- Build and maintain infrastructure for data ingestion, synchronization, and transformation across diverse hardware and simulation platforms
- Develop high-performance simulation and digital twin software, leveraging modern environments such as MuJoCo, Isaac Sim, or Newton
- Design modular APIs that abstract simulator-specific complexity and integrate with learning and control pipelines
- Ensure robust software quality through testing, profiling, and debugging of complex, performance-sensitive codebases
- Collaborate with ML, backend, systems, and hardware teams to align simulation capabilities with real-world deployment needs
- Contribute to sim-to-real workflows, including environment randomization, configuration management, and scenario generation