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 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 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.