Machine Learning Engineer - Senior

cyberwave digital agency; Zürich; Hybrid; CHF 120.000 - 150.000; Vollzeit

Benefits dieser Stelle

Flexible work culture

Equity options

Remote work support

Zusammenfassung

Cyberwave, a Zurich-based AI robotics team, seeks a Machine Learning Engineer to develop and deploy ML models for real-world robotics, focusing on deep learning, reinforcement learning, and sim2real transfer.

You'll work with software, robotics, and hardware teams to learn in simulation and perform in the real world, using PyTorch, MuJoCo, and ROS in a flexible, fast-paced environment.

Qualifikationen

• Degree in Computer Science, Robotics, AI, or related field with strong mathematical foundation.
• 3+ years of hands-on ML/deep learning model development and deployment.
• Expertise in reinforcement learning and sim-to-real transfer techniques.
• Practical experience with physics-based simulators and robotics hardware.

Aufgaben

• Develop deep learning and reinforcement learning policies for perception, control, and decision-making.
• Design and optimize ML models for real-world robotics in dynamic environments.
• Develop RL agents in simulated environments (MuJoCo, Isaac Sim, PyBullet).
• Lead sim2real transfer with domain randomization and robust policy learning.
• Deploy end-to-end ML pipelines integrated with robotics and embedded systems.

Kenntnisse

Python

C++

Reinforcement learning

Deep learning

Robotics

Experimentation

Ausbildung

BSc/ MSc/ PhD in CS/ Robotics/ AI

Tools

PyTorch

TensorFlow

JAX

MuJoCo

Isaac Sim

PyBullet

ROS / ROS 2

Jobbeschreibung

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++PyTorchTensorFlowJAXDeep LearningReinforcement LearningSim2real TransferMuJoCoIsaac SimDomain RandomizationControl TheoryComputer VisionSLAM

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

Responsibilities

• 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 MuJoCo, 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

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