Simulation & Digital Twin Engineer
Descrizione dell'offerta
Responsible for developing high-fidelity simulation environments and scalable digital twins to accelerate humanoid robotics development and deployment. The role focuses on building modular assets (robots, sensors, scenes) and enabling efficient simulation pipelines for reinforcement learning, testing, and validation. It bridges simulation and real-world systems by supporting sim-to-real transfer, synthetic data generation, and cross-team integration with control, AI, and mechanical engineering. Emphasis is placed on performance optimization, reproducibility, and creating structured environments suitable for parallel experimentation and training.
Responsibilities
- Design, build and maintain physically realistic simulation environments and
- robot models for humanoid tasks (manipulation, navigation, interaction)
- Develop modular Digital Twin assets (robot, sensors, scenes) using Isaac Sim / USD
- Enable rapid simulation cycles by:
- Structuring environments for RL compatibility
- Optimizing simulation performance for scalability and parallel training
- Implement synthetic data generation pipelines (state, vision, teleoperation data)
- Collaborate closely with multidisciplinary teams including: Motion and control
- Engineers, Mechanical design team, AI / learning engineers
- Contribute to sim-to-real alignment.
Requirements
- Degree in Robotics, Automation, Computer Science or related field.
- At least 2 years experience in robotics simulation, physics-based environments, or
- game engines
- Strong Python (C++ or similar languages is a plus)
- Experience building environments and benchmarks using simulators such as Isaac
- Sim , MuJoCo, PyBullet, Unreal or Unity
- Professional English level
- Familiarity with:
- Simulation pipelines (assets, scenes, sensors simulation)
- Kinematics and basic dynamics
- Basic rendering or physics concepts
Nice to Have
- Experience with USD / Omniverse ecosystem
- Exposure to synthetic data generation
- Experience supporting RL training environments