Research Engineer (Robotics Engineer - Learning-Based Manipulation) - LYB4

Role Overview

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have opportunities to tackle real-world, industry-relevant problems, working alongside faculty members and professional officers.

The SIT Centre for Intelligent Robotics (CIR) is an applied research centre responsible for developing and maintaining core applied robotics and embodied AI capabilities for real-world deployment. CIR is seeking a Robotics Engineer to implement state-of-the-art learning-based robotic manipulation methods on physical platforms for industry-driven applications, with an emphasis on contact-rich manipulation, robustness, and safe operation. The successful candidate will work closely with physical robots to translate recent advances in learning-based robotics into reliable, deployable systems.

Key Responsibilities

• Implement, adapt, and evaluate state-of-the-art learning-based methods (e.g. imitation learning) for contact-rich robotic manipulation.
• Integrate hardware and software components into a functional system for real-world deployment.
• Design and execute field trials (test protocols, data logging, troubleshooting, reliability improvements).
• Produce technical documentation, reports, and implementation guides to support knowledge transfer and maintainability.
• Support project procurement activities (e.g., technical specifications, vendor evaluation, quotations) for robotics hardware, sensors, and compute resources.
• Contribute to publications and technical notes to disseminate research outcomes.
• To communicate and liaise with any internal and external stakeholders to ensure project deliverables are met.
• Any other ad-hoc duties assigned by supervisor.

Requirements

• Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related field.
• Experience in imitation learning and reinforcement learning for robotics applications.
• Hands-on familiarity with robotic manipulators and motion planning.
• Proficiency in deep learning frameworks such as PyTorch or TensorFlow.
• Experience with ROS / ROS 2 for real-robot software integration.
• Proficiency in Python and C++, with good software engineering practices (version control, testing, maintainable code).
• Familiarity with robotics simulation tools such as Gazebo, Isaac Sim, or MuJoCo.
• At least 2 years of experience in robotics research or industry projects.