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Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- 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
Requirements
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
Benefits
Comp & perks- Identity verification
- Weekly payment via PayPal or Stripe
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
MuJoCoReinforcement LearningPPOSACTD3PythonPyTorchJAXrobot kinematicsrobot dynamics
Soft Skills
self-directeddetail-orientedindependent workstrong written communication