Robotics ML Expert (MuJoCo Environments) | Remote Contract
Budget: $50 – $0 USD
Project Overview
We are looking for highly experienced Robotics Machine Learning Experts to work on advanced AI simulation and reinforcement learning projects focused on MuJoCo environments.
This is a high-level remote contract opportunity where you will help design, build, and optimize physics-based simulation environments used to train intelligent agents. The work directly contributes to cutting-edge research in robotics, embodied AI, and sim-to-real learning systems.
You will be working on tasks involving locomotion, manipulation, multi-agent systems, and reinforcement learning pipelines used to train next-generation AI systems.
Compensation
$100 – $150 per hour
Remote contract role
Flexible working hours
10–40 hours per week
Long-term extension opportunities based on performance
Responsibilities
Design and develop MuJoCo simulation environments for robotics AI training
Implement and optimize reinforcement learning algorithms (PPO, SAC, TD3, etc.)
Define and tune reward functions, observation spaces, and action spaces
Debug physics simulations including contact dynamics and actuator behavior
Work with MJCF/XML model files and environment configurations
Evaluate trained policies for robustness and sim-to-real transfer potential
Document experiments, environment design, and training procedures clearly
Collaborate with remote research and engineering teams
Stay updated with advancements in robotics, RL, and embodied AI
Required Skills
Strong hands-on experience with MuJoCo or similar simulators (dm_control, Gymnasium Robotics)
Deep understanding of Reinforcement Learning (RL) algorithms
Strong Python programming skills
Experience with PyTorch or JAX
Knowledge of robot kinematics, dynamics, and control systems
Experience designing reward functions for complex tasks
Ability to work with MJCF/XML simulation files
Strong analytical and debugging skills
Ability to work independently in a remote environment
Strong technical communication and documentation skills
Preferred Experience
Sim-to-real transfer techniques (domain randomization, system identification)
Experience with Isaac Gym, PyBullet, Drake, or Genesis
Multi-agent reinforcement learning
Imitation learning or model-based RL
Research publications in robotics or machine learning
Open-source contributions in RL or robotics frameworks
Graduate-level education in Robotics, ML, or Computer Science
Ideal Candidate
You are a strong fit if you:
Have hands-on experience building RL environments
Understand how physics simulation impacts learning performance
Can design reward systems that produce stable and transferable policies
Are comfortable working independently in research-style environments
Enjoy solving complex problems in robotics and AI systems
Why Join This Role?
Work on cutting-edge robotics and AI research problems
Fully remote and flexible contract structure
High-impact work influencing real-world AI systems
Collaborate with top-tier global ML and robotics practitioners
Opportunity for long-term contract extensions and new research projects
Work at the intersection of simulation, robotics, and reinforcement learning
Application Instructions
To apply, please include:
Brief introduction and relevant experience
Details of MuJoCo or simulation-based projects you’ve worked on
Experience with RL algorithms and frameworks
Python and ML framework expertise (PyTorch/JAX)
Any research papers, GitHub, or portfolio links (if available)
LinkedIn profile (optional)
Applications are reviewed on a rolling basis. Qualified candidates may be contacted for further technical evaluation.
We are looking for highly experienced Robotics Machine Learning Experts to work on advanced AI simulation and reinforcement learning projects focused on MuJoCo environments.
This is a high-level remote contract opportunity where you will help design, build, and optimize physics-based simulation environments used to train intelligent agents. The work directly contributes to cutting-edge research in robotics, embodied AI, and sim-to-real learning systems.
You will be working on tasks involving locomotion, manipulation, multi-agent systems, and reinforcement learning pipelines used to train next-generation AI systems.
Compensation
$100 – $150 per hour
Remote contract role
Flexible working hours
10–40 hours per week
Long-term extension opportunities based on performance
Responsibilities
Design and develop MuJoCo simulation environments for robotics AI training
Implement and optimize reinforcement learning algorithms (PPO, SAC, TD3, etc.)
Define and tune reward functions, observation spaces, and action spaces
Debug physics simulations including contact dynamics and actuator behavior
Work with MJCF/XML model files and environment configurations
Evaluate trained policies for robustness and sim-to-real transfer potential
Document experiments, environment design, and training procedures clearly
Collaborate with remote research and engineering teams
Stay updated with advancements in robotics, RL, and embodied AI
Required Skills
Strong hands-on experience with MuJoCo or similar simulators (dm_control, Gymnasium Robotics)
Deep understanding of Reinforcement Learning (RL) algorithms
Strong Python programming skills
Experience with PyTorch or JAX
Knowledge of robot kinematics, dynamics, and control systems
Experience designing reward functions for complex tasks
Ability to work with MJCF/XML simulation files
Strong analytical and debugging skills
Ability to work independently in a remote environment
Strong technical communication and documentation skills
Preferred Experience
Sim-to-real transfer techniques (domain randomization, system identification)
Experience with Isaac Gym, PyBullet, Drake, or Genesis
Multi-agent reinforcement learning
Imitation learning or model-based RL
Research publications in robotics or machine learning
Open-source contributions in RL or robotics frameworks
Graduate-level education in Robotics, ML, or Computer Science
Ideal Candidate
You are a strong fit if you:
Have hands-on experience building RL environments
Understand how physics simulation impacts learning performance
Can design reward systems that produce stable and transferable policies
Are comfortable working independently in research-style environments
Enjoy solving complex problems in robotics and AI systems
Why Join This Role?
Work on cutting-edge robotics and AI research problems
Fully remote and flexible contract structure
High-impact work influencing real-world AI systems
Collaborate with top-tier global ML and robotics practitioners
Opportunity for long-term contract extensions and new research projects
Work at the intersection of simulation, robotics, and reinforcement learning
Application Instructions
To apply, please include:
Brief introduction and relevant experience
Details of MuJoCo or simulation-based projects you’ve worked on
Experience with RL algorithms and frameworks
Python and ML framework expertise (PyTorch/JAX)
Any research papers, GitHub, or portfolio links (if available)
LinkedIn profile (optional)
Applications are reviewed on a rolling basis. Qualified candidates may be contacted for further technical evaluation.