Robotics Simulation Engineer

Location: Chennai
Experience: 6-8 years
Qualification: Bachelor's or Master's degree in Robotics, Computer Science, Mechatronics, Mechanical Engineering, Electrical Engineering, or a related discipline.
Open Positions: 01

Role Summary

The Robotics Simulation Engineer will design, develop, test, and virtually validate high-fidelity robotic simulation pipelines within the NVIDIA Isaac ecosystem. The role combines physics-based simulation, reinforcement learning, foundation-model integration, ROS 2 middleware, GPU containerization, and sim-to-real engineering to support reliable deployment of robotic applications in an advanced manufacturing environment.

Key Responsibilities

• Architect high-fidelity, physics-based simulation environments using NVIDIA Isaac Sim, Isaac Labs, and Omniverse.
• Create and maintain accurate USD-based robot, sensor, workstation, and factory-scene assets, including RGB-D, LiDAR, IMU, contact, and force-torque sensor models.
• Develop scalable reinforcement-learning pipelines in Isaac Gym or Isaac Labs using PyTorch, including reward design, policy architecture, parallel training, and performance optimization.
• Integrate and fine-tune embodied-AI or foundation models such as NVIDIA GR00T for manipulation, locomotion, and related robotics use cases.
• Design ROS 2 packages, nodes, lifecycle components, custom messages, action servers, and DDS communication interfaces between simulation and robot-control stacks.
• Develop modular Python automation, custom Omniverse/USD extensions, synthetic-data generation workflows, and reusable engineering tools.
• Build production-grade Docker images using CUDA, NVIDIA Container Toolkit, and compatible Omniverse runtimes for headless GPU execution.
• Implement domain randomization across visual, dynamic, friction, latency, payload, and sensor-noise parameters to improve model robustness.
• Establish software-in-the-loop and hardware-in-the-loop validation frameworks and perform system identification using real robot telemetry.
• Support deployment across local GPU workstations and approved cloud environments, with automated simulation and regression testing in CI/CD pipelines.

Required Technical Skills

• Strong hands-on experience with NVIDIA Isaac Sim and Isaac Labs; working knowledge of Isaac Gym and NVIDIA Omniverse.
• Advanced Python development skills and practical C++ experience (C++17 or later) for robotics and performance-critical components.
• Solid understanding of reinforcement learning, PyTorch, reward engineering, policy training, inference optimization, and experiment evaluation.
• Proficiency in ROS 2, DDS, URDF, robot kinematics and dynamics, motion/control interfaces, and distributed robotics software design.
• Hands-on experience with Universal Scene Description (USD), OpenUSD APIs, URDF and/or MJCF asset workflows.
• Experience with Docker, NVIDIA Container Toolkit, CUDA-enabled workloads, Linux, Git, and CI/CD automation.
• Knowledge of sim-to-real methods, domain randomization, sensor modeling, system identification, and validation methodology.

Preferred Experience

• Experience with NVIDIA GR00T or other vision-language-action / embodied-AI foundation models.
• Exposure to TensorRT, Warp, PhysX, synthetic-data generation, and performance profiling of GPU-accelerated simulations.
• Experience deploying simulation workloads on multi-GPU workstations or cloud platforms such as AWS, Azure, or Google Cloud.
• Background in industrial robotics, autonomous systems, digital manufacturing, or virtual commissioning.

Education and Experience

• Bachelor's or Master's degree in Robotics, Computer Science, Mechatronics, Mechanical Engineering, Electrical Engineering, or a related discipline.
• Typically 6-8 years of relevant engineering experience, including demonstrable ownership of complex robotics simulation or autonomy projects.
• A portfolio, code samples, technical publications, or documented project outcomes demonstrating simulation and robot-learning expertise is desirable.

Key Deliverables

• Validated Isaac Sim/Omniverse environments and reusable USD scene graphs.
• Reproducible RL training pipelines, trained-policy checkpoints, evaluation results, and model configuration assets.
• Tested ROS 2 packages, launch files, middleware interfaces, and controller-integration components.
• Portable GPU-enabled container images and automated build, simulation, and regression-test workflows.
• Sim-to-real verification evidence, benchmarking results, system-identification outputs, and deployment-readiness recommendations.
• Complete technical documentation, setup guides, architecture notes, SOPs, and structured handover materials.

Behavioral Competencies

• Strong analytical problem-solving and systematic debugging skills.
• Ability to translate engineering objectives into maintainable simulation and software architectures.
• Clear written and verbal communication with cross-functional engineering stakeholders.
• Ownership mindset, attention to quality, and discipline in configuration management and documentation.
• Ability to work independently while collaborating effectively in an agile, milestone-driven environment.

Success Measures

• Simulation fidelity, stability, reproducibility, and performance meet agreed acceptance criteria.
• Robot-learning and foundation-model workflows execute reliably and demonstrate measurable task performance.
• ROS 2 middleware and controls interfaces pass functional and integration validation.
• Containerized environments are portable, documented, and suitable for repeatable local or cloud execution.
• Sim-to-real risk is reduced through documented evidence, controlled testing, and actionable engineering recommendations.

Send your CVs to [email protected]