Technical jobs in Singapore

Robotics Engineer (Simulation for Embodied AI)

As a University of Applied Learning, Singapore Institute of Technology (SIT) works closely with industry partners to develop applied research capabilities that translate directly into real-world deployment. Our research staff are equipped with industry-relevant skills through hands-on work on operational research platforms and systems.

The SIT Centre for Intelligent Robotics (CIR) is a strategic capability centre under RoboPrecinct@Punggol Digital District (PDD), responsible for developing and sustaining the simulation, digital twin, and embodied AI foundations required for precinct-level deployment of robotic systems.

We are seeking a Robotics Simulation Engineer to support the Centre’s robotics and embodied AI initiatives. This role focuses on the design, implementation, and maintenance of multi-platform robotics simulation environments, supporting research, development, testing, and Sim2Real transfer of robotic systems. The successful candidate will work across NVIDIA Isaac Sim, Gazebo, MuJoCo, and related simulation platforms, enabling scalable experimentation and robust system-level evaluation prior to real-world deployment.

• Develop, configure, and maintain robotics simulation environments across multiple platforms, including NVIDIA Isaac Sim, Gazebo, MuJoCo, and other relevant simulators for different applications and robotics platform
• Design modular and reusable simulation assets for robots, environments, sensors, and physics configurations.
• Integrate robotic platforms into simulation, including URDF/SDF workflows, articulation setup, and validation of kinematics and dynamics.
• Configure and tune physics simulation parameters (contacts, friction, joint limits, control rates, solver settings) to ensure realistic robot behaviour.
• Develop and maintain sensor simulation pipelines (RGB, depth, RGB-D, LiDAR, IMU) for perception and learning-based robotics.
• Support simulation-to-real (Sim2Real) workflows, including domain randomization, sensor noise modelling, and reality-gap mitigation.
• Integrate simulations with ROS / ROS 2 for closed-loop robot control, testing, and benchmarking.
• Automate simulation setup, execution, and experiment management using Python and C++, supporting batch experiments and data collection.
• Support system-level experimentation, benchmarking, and iterative improvement of embodied AI and robotic applications.
• Prepare technical documentation, simulation guidelines, and reusable templates to support knowledge transfer and long‑term maintainability.
• Provide mentorship to undergraduate students in projects.
• Carry out risk assessment, and ensure compliance with Work, Safety and Health Regulations.
• Prepare technical documentation, reports, and implementation guides to support knowledge transfer and long‑term maintainability.
• Contribute to publications and technical notes to disseminate research outcomes.
• The employee is to communicate with any relevant internal or external stakeholders to ensure project deliverables are met.
• Any other adhoc duties assigned by supervisor.

• Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related discipline.
• Hands‑on experience with at least two robotics simulation platforms, such as NVIDIA Isaac Sim, Gazebo, or MuJoCo.
• Practical experience working with URDF and/or SDF robot models and simulation integration.
• Working knowledge of robotics physics and dynamics, including articulated systems.
• Experience integrating simulation environments with ROS or ROS 2.
• Proficiency in Python, with working knowledge of++ for simulation or robotics software development.
• Minimum 2 years of experience in robotics simulation, robotics research, or industry‑based robotics projects.

• Experience with USD‑based workflows and Omniverse‑based simulation environments.
• Familiarity with PhysX and/or other physics engines used in robotics simulation.
• Experience supporting Sim2Real pipelines for real‑robot deployment.
• Exposure to reinforcement learning, imitation learning, or learning‑based control in simulation.
• Experience working with mobile manipulators, humanoid robots, or legged robots.
• Familiarity with GPU‑accelerated simulation, performance optimisation, and Linux‑based systems.