Role Summary

Job Description

The mission of the Adaptive Robotics and Mechatronics (ARM) group in the Singapore Institute of Manufacturing Technology (SIMTech) is to advance the state of intelligent, adaptive, and embodied robotic systems for next-generation manufacturing.

We are seeking a Research Engineer/Senior Research Engineer to develop embodied intelligence solutions for robotic manipulation as well as deploy into real-world industrial applications. This role focuses on developing the perception/learning-based action models into reliable, real-time robotic systems that operate in complex, unstructured industrial environments.

You will work at the intersection of perception, decision-making, and physical robot programming and control, building integrated pipelines that allow robots to sense, reason, and act through interaction with the physical world. Working closely with robotics, machine learning, and sensing experts, you will bring embodied AI from laboratory prototypes to production-ready robotic platforms.

Responsibilities:

• Design and develop perception pipelines that fuse vision, depth, and other sensory inputs for robot-centric understanding of objects and environments.
• Implement and integrate learning-based models (e.g., vision-language-action, policy networks, or multimodal models) that enable robots to choose actions based on perception and task context.
• Develop robot motion/trajectory planning and control solution using tools such as ROS1/ROS2, MoveIt, Nvidia Issac, etc for robot simulation and control.
• Build and iterate on prototypes to validate algorithms and concepts in real-world robotic applications, ensuring robustness and scalability.

Job Requirements

• Bachelor’s or Master’s degree in Robotics, Computer Engineering, Mechanical Engineering, Computer Science, Automation, or related fields.
• Strong programming skills in Python and/or C++, with experience building robotics software systems.
• Experience with robotics frameworks such as ROS and robot middleware for sensor and controller integration.
• Familiarity with robot kinematics, motion planning, and control, especially for manipulation systems.
• Exposure to learning-based robotic control, such as imitation learning, reinforcement learning, or multimodal policy models.
• Ability to bridge machine learning models and physical robot execution.