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. 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. Tagged as: AI, Bash, C, C#, C++, docker, Go, Golang, Java, JavaScript, junior, machine learning, mid-level, PHP, Python, Rust, senior, Typescript