Robotics ML Expert, AI

Role Description

• 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.

Qualifications

• 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

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 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.