Job Description :
An Isaac Sim expert has deep knowledge of NVIDIA's robotics simulation platform and its integration into robotics and AI workflows. This expertise covers building, testing, and training AI-driven robots in physically realistic virtual environments using the NVIDIA Omniverse platform .
Core areas of expertise : -
An Isaac Sim expert is skilled in a wide range of tasks and technologies essential for advanced robotics simulation :
- Physics simulation : Tuning and optimizing the high-fidelity, GPU-accelerated PhysX engine for realistic robot behavior.
- Synthetic data generation (SDG) : Using NVIDIA Omniverse Replicator to generate large, labeled datasets for training perception models. This includes randomizing scenes, objects, and lighting to create diverse data.
- Digital twins : Creating precise virtual replicas of real-world environments, such as factory floors, to design and validate robot applications before real-world deployment.
- Robot learning : Developing and accelerating reinforcement learning (RL) and imitation learning algorithms using the GPU-accelerated Isaac Lab framework.
- Sensor simulation : Accurately simulating a variety of sensors, including cameras, LiDAR, and contact sensors, with features like RTX real-time ray and path tracing.
- Robotics integration : Bridging the simulation to real-world robots using communication protocols like ROS and ROS 2.
- Workflow scripting : Using Python and the Core API for a wide range of tasks, from building environments to scripting complex robot behaviors.
- USD and Omniverse : Leveraging the Universal Scene Description (OpenUSD) file format to import, build, and share robot and environment assets.
Key skills for an Isaac Sim expert : -
Recruiters and project managers seeking an expert in this field often look for the following skills :
Technical proficiency : Deep expertise in Python and / or C++ and extensive experience with Isaac Sim and the Omniverse platform .Robotics fundamentals : A strong background in physics, kinematics, motion planning, and 3D modeling.Machine learning : Knowledge of training and deploying AI models , particularly in the context of robot perception and control.System integration : Experience integrating different software components and hardware into a cohesive robotics system.Troubleshooting : The ability to debug complex issues related to physics, integration, and simulation performance.