Robotics ML Expert, AI

Before applying

This role is open to contractors in accepted locations only. Please confirm your country is on the list before applying — we're unable to process applications from unlisted locations. List of accepted countries and locations. https://docs.google.com/document/d/1FK0v1X3O3rqY0oB2k5xt0u5eiYaoYYKv_E4XS3kHXUs/edit?tab=t.0#heading=h.8jwvoue7ks7z

For US applicants

This is a 1099 independent contractor role. It is not compatible with F-1 OPT, STEM OPT, or any visa status that requires W-2 employment, guaranteed hours, or employer sponsorship.

We are unable to provide offer letters or employment verification for this role.

WHAT YOU'LL BE DOING

- Design, build, and iterate on MuJoCo simulation environments for robotics research and AI training

- Implement and tune RL algorithms (PPO, SAC, TD3) to train agents on simulated tasks

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

- Debug and optimize physics simulations — contact models, actuator dynamics, scene configs

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

- Document environment specs, training procedures, and experimental results clearly

- Collaborate async with research teams and stay current with advances in robot learning and embodied AI

RLHF in one line: Generate code → expert engineers rank, edit, and justify → convert that feedback into reward signals → reinforcement learning tunes the model toward code you'd actually ship.

WHAT YOU'LL NEED

- Strong hands-on experience with MuJoCo (or via dm_control, Gymnasium-Robotics, or similar)

- Solid understanding of RL theory and practical training pipelines

- Proficient in Python + ML frameworks (PyTorch or JAX)

- Experience defining reward functions for complex robotic tasks

- Familiar with robot kinematics, dynamics, and control fundamentals

- Can read and write MJCF/XML model files and understand their physics impl...