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 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.

G2i Inc.

Open Positions (1)

Other

Robotics ML Expert, AI