5 days ago

Robotics ML Expert — MuJoCo & RL Training

Alignerr

Amsterdam

5 days ago

Apply

Alignerr is seeking Robotics ML Experts in the Netherlands with hands-on MuJoCo experience to design, build, and refine simulation environments for training AI systems in robotics tasks. This is a fully remote, flexible hourly contract role requiring strong reinforcement learning and Python skills, with a commitment of 10–40 hours per week.

Core AIRemoteContractEntry LevelMuJoCoReinforcement Learning

Robotics ML Expert — MuJoCo & RL Training

Apply

Core AIRemoteContractEntry LevelMuJoCo

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Fully remote, flexible

Employment Type

Hourly Contract

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

MuJoCoReinforcement LearningPythonPyTorchJAXReward FunctionsRobot KinematicsRobot DynamicsRobot ControlMJCFXMLPPOSACTD3

Soft Skills

Self-directedDetail-orientedStrong written communicatorCollaborationIndependence

Preferred Qualifications

Technical (Nice-to-have)

Sim-to-Real TransferDomain RandomizationSystem IdentificationIsaac GymPyBulletDrakeGenesisMulti-Agent EnvironmentsHierarchical RLImitation LearningModel-Based RLWorld Models

Key Responsibilities

•Design, develop, and iterate on MuJoCo simulation environments for robotics research and AI training
•Implement and tune reinforcement learning algorithms (PPO, SAC, TD3, etc.) to train agents in simulated tasks
•Define reward functions, observation spaces, and action spaces that produce robust, transferable policies
•Debug and optimize physics simulations — contact models, actuator dynamics, and scene configurations
•Evaluate trained policies for stability, generalization, and sim-to-real transfer potential
•Document environment specifications, training procedures, and experimental results clearly and thoroughly
•Collaborate asynchronously with research teams to align simulation work with broader project goals
•Stay current with the latest advances in robot learning, simulation, and embodied AI

RoboticsMachine LearningMuJoCoReinforcement LearningSimulationRemoteContractAI TrainingPythonPyTorch

Key Responsibilities

•Design, develop, and iterate on MuJoCo simulation environments for robotics research and AI training

•Implement and tune reinforcement learning algorithms (PPO, SAC, TD3, etc.) to train agents in simulated tasks

•Define reward functions, observation spaces, and action spaces that produce robust, transferable policies

•Debug and optimize physics simulations — contact models, actuator dynamics, and scene configurations

•Evaluate trained policies for stability, generalization, and sim-to-real transfer potential

•Document environment specifications, training procedures, and experimental results clearly and thoroughly

•Collaborate asynchronously with research teams to align simulation work with broader project goals

•Stay current with the latest advances in robot learning, simulation, and embodied AI