G
G2Ivia Ashby
Robotics ML Expert, AI
REMOTEPosted 3w ago
RoboticsMid LevelFull-time#remote
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About the Role
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 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.
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 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.
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