Real-World Robotics AI Engineer: Train & Deploy on Robots

MENLO RESEARCH PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Menlo Research is seeking a Robotics AI Engineer to design and train policies that run on Asimov in the real world. You will work at the intersection of learning and hardware, iterating quickly with hardware, firmware, and infrastructure teams to close the loop between training and deployment.

You will build simulation environments, run experiments on physical robots, and push for robust, real-world policy performance with a strong emphasis on practical results over pure simulation.

Qualifications

• Hands-on experience training policies that run on real systems.
• Comfort working directly with robots, not just simulators.
• Proficiency in Python and familiarity with RL/ML frameworks (JAX, PyTorch, IsaacGym/IsaacLab, MuJoCo).
• Experience with sim-to-real transfer techniques is a plus.

Responsibilities

• Design and train RL and imitation learning policies for locomotion, manipulation, or whole-body control.
• Run experiments on physical hardware and close the sim-to-real gap through debugging and domain adaptation.
• Build and maintain simulation environments and data pipelines that support fast policy iteration.
• Instrument robot deployments and analyze failure modes to feed improvements back into training.
• Collaborate with hardware and firmware engineers to understand physical constraints and improve policy robustness.

Skills

Reinforcement learning

Imitation learning

Python

RL frameworks

Tools

IsaacGym/IsaacLab

MuJoCo

Job description

Menlo Research is seeking a Robotics AI Engineer to design and train policies that run on Asimov in the real world. You will work at the intersection of learning and hardware, iterating quickly with hardware, firmware, and infrastructure teams to close the loop between training and deployment.

You will build simulation environments, run experiments on physical robots, and push for robust, real-world policy performance with a strong emphasis on practical results over pure simulation.