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.