Job Description

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.

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 implications

• Self-directed, detail-oriented, comfortable working independently in an async environment

• Strong written communicator — a big part of this role is explaining your reasoning clearly

Identity verification: Applicants will be required to verify their identity and confirm they have valid documentation to work as an independent contractor in their country of residence.

Nice to Have

• Experience with sim-to-real transfer — 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 a degree in robotics, ML, CS, or a related field

What You Don't Need

• No prior RLHF or AI training experience

• No deep machine learning knowledge — if you can review and critique code clearly, we'll teach you the rest

Logistics

• Location: Fully remote — work from anywhere on the accepted locations list

• Compensation: $30–$70/hr based on location and seniority. Note: the majority of projects run at around $30/hr — higher rates apply to senior profiles and specific project types

• Hours: Minimum 15 hrs/week, up to 40+ hrs/week available — hours vary by project and are not guaranteed week to week

• Engagement: 1099 independent contractor

• Payment: Weekly via PayPal or Stripe

⚠️ Important: Hours are project-dependent and can vary week to week. We recommend keeping other work options open alongside this engagement rather than relying on it as your sole source of income.