Architect Jobs

About The Position

Requirements

• Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field
• Proven experience in simulation architecture using platforms such as NVIDIA Isaac Sim, Gazebo, MuJoCo, Webots, or Unreal Engine, tailored to specific robotic applications.
• Strong knowledge and hands-on experience developing and maintaining digital twin models for robotic systems, including real-time state synchronization and physics calibration.
• Experience designing domain randomization strategies, sensor noise modeling, and physics engine calibration to bridge the gap between simulation and real-world deployment.
• Expertise in architecting and deploying simulation environments on cloud platforms including containerized and distributed simulation workflows.
• Experience integrating machine learning frameworks (PyTorch, TensorFlow, Isaac Lab, or similar) into simulation pipelines for policy training, synthetic data generation, and autonomous behavior development.
• Hands-on experience designing and executing Software-in-the-Loop and Hardware-in-the-Loop testing workflows, including real-time simulation interfacing and embedded system integration.
• Proven experience with ROS and ROS2 in simulation and real-world robotic deployments.
• Excellent problem-solving skills and attention to detail in debugging complex hardware-software-simulation interactions.
• Ability to work collaboratively in a cross-functional team environment with strong written and verbal communication skills.

Responsibilities

• Collaborate with cross-functional teams to build and refine robust pipelines for transferring learned behaviors, sensor models, and physics parameters between simulation and real-world robotic systems, minimizing the reality gap through domain randomization and adaptation techniques.
• Architect and deploy scalable simulation infrastructure on cloud platforms, ensuring seamless parallel simulation runs, distributed training environments, and on-demand access optimized for robotic use cases with low latency and high performance.
• Provide technical leadership in integrating AI/ML frameworks into simulation environments, enabling reinforcement learning, synthetic data generation, autonomous behavior training, and intelligent environment modeling.
• Design and implement SIL testing frameworks to validate robotic software stacks — including perception, planning, and control modules — within simulated environments.
• Develop and maintain HIL testing setups that interface real hardware components with simulated environments, ensuring real-time performance validation, system integrity, and hardware-software compatibility verification.
• Stay updated with the latest trends and technologies in robotics simulation frameworks, automation, AI/ML integration, and sim-to-real methodologies, offering insights and recommendations to continuously enhance simulation capabilities.

Benefits

• AMD benefits at a glance