About the role
Role Overview
As Singapore’s University of Applied Learning, SIT works closely with industry and research partners in pursuing applied research with real-world impact. Our research staff have opportunities to develop industry-relevant capabilities while contributing to multidisciplinary research projects.
The primary responsibility of this role is to support SIT’s research activities under a programme focused on robotics applications in the aviation industry. The Research Engineer will contribute to the end-to-end robot learning pipeline for manipulation tasks, including data collection, policy training, evaluation, and deployment on real robotic systems.
Key Responsibilities
- Develop, implement, and evaluate learning-based manipulation methods, including imitation learning, reinforcement learning, and related approaches for contact-rich robotic tasks.
- Own the end-to-end robot learning pipeline, from demonstration/data collection and dataset preparation to policy training, evaluation, real-robot deployment, and iterative improvement.
- Develop real-world data collection pipelines using teleoperation or other demonstration interfaces, including sensing, synchronization, logging, and data quality control.
- Train and deploy learned policies on robotic manipulators and mobile manipulation platforms, and troubleshoot performance issues across perception, control, learning, and hardware.
- Integrate robot hardware, sensors, compute platforms, perception, motion planning, and learning components into robust robotic systems.
- Design and conduct laboratory and field evaluations, including performance benchmarking, failure analysis, and reliability improvement.
- Work with researchers and industry partners to translate research into practical robotic capabilities, and contribute to technical documentation, publications, demonstrations, and project outcomes.
- Support technical evaluation and procurement of robotic platforms, sensors, compute infrastructure, and related equipment where required.
- To communicate and liaise with internal and external stakeholder to ensure project deliverable are met.
- Any other ad-hoc duties assigned by Supervisor.
Requirements
- Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, Control, Artificial Intelligence, or a related field.
- Relevant experience in robotics research, development, or industry, with substantial hands-on work on physical robotic systems.
- Demonstrated experience developing and deploying learning-based manipulation methods on real robots, rather than simulation-only experimentation.
- Practical experience with imitation learning, behavioural cloning, diffusion policies, reinforcement learning, or related robot learning approaches.
- Experience collecting and working with real-world robot demonstration data, including teleoperation or other human-in-the-loop methods.
- Strong hands-on experience with robotic manipulators, ROS / ROS 2, motion planning, calibration, trajectory execution, and real-robot debugging.
- Proficiency in PyTorch and Python, with working knowledge of C++ and good software engineering practices.
- Familiarity with robotics simulation environments such as Isaac Sim / Isaac Lab, MuJoCo, Gazebo, or equivalent.
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