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Robotics Simulation Engineer

Ceinsys Tech -
Chennai, Tamil Nadu

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Job details

2 days ago

Qualifications

CI/CD
Mechanical engineering
Azure
Computer Science
Software design
PyTorch
Git
Google Cloud Platform
Master's degree
AWS
Analysis skills
C++
Docker
C
Bachelor's degree
Continuous integration
APIs
Agile
Linux
Manufacturing
AI
Robotics
Mechatronics
Communication skills
Python
Debugging

Full job description

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]

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