Job Description
Responsibilities
- Design and train reinforcement learning and imitation learning policies for movement and control tasks
- Run experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptation
- Build and maintain simulation environments and data pipelines that support fast policy iteration
- Instrument deployments and analyse failure modes, feeding what you learn back into training
- Work closely with hardware and firmware engineers to understand physical constraints and improve policy robustness
Requirements
- Around 2 to 3 years of relevant experience; exceptional recent graduates with a genuinely strong portfolio and internship background will also be considered
- Strong foundations in reinforcement learning or imitation learning, with hands-on experience training policies that run on real physical systems (not simulation only)
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Job Details
- Location Singapore, Singapore
- Job Type Full-time
- Category computer-and-mathematical
- Posted Date July 01, 2026
- Application Deadline August 10, 2026