Robotics ML Expert — MuJoCo Environments

About The Role

What if your expertise in robotics and machine learning could directly shape how the next generation of intelligent agents learn to move, manipulate, and interact with the physical world? We're looking for Robotics ML Experts with hands-on MuJoCo experience to design, build, and refine simulation environments that train AI systems to perform real-world tasks — from locomotion and dexterous manipulation to complex multi-agent coordination. This is a fully remote, flexible contract role for experienced practitioners who live and breathe physics simulation, reinforcement learning, and robot control. If you've spent time wrangling MJCF files, tuning reward functions, and debugging contact dynamics, this role was made for you.

Organization

Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week

What You'll Do

• 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

Who You Are

• Strong hands-on experience with MuJoCo (or MuJoCo via dm_control, Gymnasium/Gymnasium-Robotics, or similar wrappers)
• Solid understanding of reinforcement learning theory and practical training pipelines
• Proficient in Python and comfortable with ML frameworks such as PyTorch or JAX
• Experienced in defining and shaping reward functions for complex robotic tasks
• Familiar with robot kinematics, dynamics, and control fundamentals
• Able to read and write MJCF/XML model files and understand their physics implications
• Self-directed, detail-oriented, and comfortable working independently in an async environment
• Strong written communicator who can document technical work clearly

Nice to Have

• Experience with sim-to-real transfer techniques (domain randomization, system identification)
• Familiarity with other physics simulators — Isaac Gym, PyBullet, Drake, or Genesis
• Background in multi-agent environments or hierarchical RL
• Published research or open-source contributions in robotics, RL, or embodied AI
• Experience with imitation learning, model-based RL, or world models
• Graduate-level coursework or degree in robotics, ML, computer science, or a related field