Robotics Reinforcement Learning Engineer – Reinforcement Learning, Sim2Real

Asiacruit

. Develop and train reinforcement learning policies for quadruped locomotion using NVIDIA Isaac Lab, Isaac Gym, RaiSim, or comparable robotics simulation environments .

Posted 10/26/2026contractSeoul • South KoreaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing reinforcement learning algorithms for quadruped locomotion, with a strong focus on real-time control and Sim-to-Real transfer. Proficient in utilizing advanced robotics simulation environments and deploying machine learning models on robotic hardware.

Highest-signal resume keywords

Reinforcement Learning AlgorithmsNVIDIA Isaac LabPPO ImplementationPython ProgrammingC++ Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningLocomotion ControlDomain RandomizationSimulation BenchmarkingNeural Network PoliciesRobotics SimulationFoot-Contact EstimationSlip DetectionMotor DynamicsReal-Time Control

Soft Skills

Analytical SkillsTroubleshooting Skills

Tools & Technologies

PyTorchONNX RuntimeLibTorchIsaac GymRaiSimMuJoCo

Industry Keywords

RoboticsAutonomous RoboticsLegged RoboticsContact-Rich ControlSim-to-Real Transfer

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Develop and train reinforcement learning policies for quadruped locomotion using NVIDIA Isaac Lab, Isaac Gym, RaiSim, or comparable robotics simulation environments
Train large-scale parallel locomotion policies across diverse terrain conditions, including uneven surfaces, varying friction, and dynamic disturbances
Implement blind and vision-assisted locomotion policies
Design and refine domain randomization strategies covering surface friction, motor latency, payload variations, mass distribution, and other physical parameters
Improve Sim-to-Real transfer for policies deployed on physical robotic hardware
Develop and optimize reinforcement learning algorithms, including PPO, using PyTorch
Deploy trained neural network policies using C++, ONNX Runtime, LibTorch, or other inference frameworks
Optimize policy inference latency and execution frequency for real-time locomotion control
Integrate foot-contact estimation, slip detection, and reflex mechanisms
Evaluate locomotion performance through simulation benchmarks and real-world testing, identify failure modes, and implement improvements
Collaborate with robotics controls, simulation, and embedded systems engineers on end-to-end robotic autonomy systems

Requirements

What you’ll need

Bachelor's or Master's degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Control Systems, or a related field
Approximately 3–5 years of relevant experience in reinforcement learning, robot learning, locomotion control, or autonomous robotics
Strong programming skills in Python and practical experience with PyTorch
Experience implementing reinforcement learning algorithms, particularly PPO or other policy optimization methods
Hands-on experience with NVIDIA Isaac Lab, Isaac Gym, RaiSim, MuJoCo, or comparable physics-based robotics simulators
Understanding of legged robotics, locomotion dynamics, robot balance, or contact-rich control
Practical knowledge of domain randomization, system identification, or other techniques for improving Sim-to-Real transfer
Experience with C++ and deploying machine learning models on robotic hardware or edge computing platforms
Familiarity with ONNX Runtime, LibTorch, or other low-latency model inference frameworks is an advantage
Understanding of foot-contact estimation, slip detection, motor dynamics, or real-time control is a plus
Strong analytical and troubleshooting skills, with the ability to investigate simulation failures and validate solutions on physical robots