Overview In this role you will design and advance robotic simulation capabilities that bridge virtual and real systems. You will lead the integration of AI and ML into simulation pipelines, and build scalable cloud-based infrastructures for fast, low-latency robotics experiments. You will collaborate with cross-functional teams to reduce the reality gap and validate robotic software stacks through SIL and HIL methodologies. This position offers the chance to shape next-generation robotic simulation and digital twin solutions in a highly innovative environment.
Responsibilities- Collaborate with cross-functional teams to build pipelines for transferring learned behaviors, sensor models, and physics parameters between simulation and real-world robots, using domain randomization to reduce the reality gap
- Architect and deploy scalable cloud-based simulation infrastructure for parallel runs, distributed training, and on-demand access with low latency
- Lead the integration of AI/ML frameworks into simulation environments to enable reinforcement learning, synthetic data generation, and autonomous behavior development
- Design and implement SIL testing frameworks to validate perception, planning, and control modules within simulated environments
- Develop and maintain HIL testing setups interfacing real hardware with simulated environments for real-time validation and hardware-software compatibility
- Stay current with robotics simulation frameworks, automation, AI/ML integration, and sim-to-real methodologies to continuously enhance capabilities
Key requirements- Proven experience with simulation platforms (NVIDIA Isaac Sim, Gazebo, MuJoCo, Webots, Unreal Engine)
- Digital twin development for robotic systems with real-time state synchronization and physics calibration
- Experience with sim-to-real and real-to-sim pipelines, domain randomization, sensor noise modeling, and physics engine calibration
- Cloud-based simulation infrastructure with containerized and distributed workflows
- AI/ML integration into simulation pipelines (PyTorch, TensorFlow, similar) for policy training and synthetic data generation
- SIL and HIL testing hands-on experience and real-time simulation interfacing
- Robotics frameworks proficiency (ROS and ROS2) in both simulation and real deployments
- Strong problem-solving and debugging skills across hardware-software-simulation
- Effective collaboration and communication in cross-functional teams
- collaboration
- communication
- problem-solving
- NVIDIA Isaac Sim
- Gazebo
- MuJoCo
