Machine Learning Engineer - Senior

Location: Zurich, Switzerland

Employment Type: Full-time

Posted: 2 days ago

About the Position

Join Cyberwave as a Senior Machine Learning Engineer to develop and deploy advanced machine learning models for robotics and control systems. Our vision is to unlock the full potential of intelligent machines by making robotics as accessible, scalable, and programmable as cloud software. We aim 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.

Responsibilities

• Develop deep learning and reinforcement learning policies for perception, control, and decision-making
• 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 MuJoCo, Isaac Lab/Sim, PyBullet, or proprietary simulators
• 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

Requirements

• 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, TensorFlow, 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., MuJoCo, 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

What We Offer

• 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

Ready to Join Our Team?

We'd love to hear from you! When applying, please include:

• Your Github or LinkedIn profile
• 2-3 lines about why you would like to join Cyberwave

Tell us what excites you about this opportunity and how you can contribute to our mission!