🛠 Tech Stack

💼 About This Role

You'll design and build MuJoCo simulation environments for robotics research and AI training. You'll implement RL algorithms and define reward functions to produce robust, transferable policies. You'll collaborate async with research teams to push the boundaries of embodied AI.

🎯 What You'll Do

• Design and build MuJoCo simulation environments for robotics research.
• Implement and tune RL algorithms like PPO, SAC, TD3.
• Define reward functions, observation spaces, and action spaces.
• Debug and optimize physics simulations — contact models, actuator dynamics.

📋 Requirements

• Strong hands-on experience with MuJoCo or dm_control.
• Solid understanding of RL theory and practical training pipelines.
• Proficient in Python and ML frameworks like PyTorch or JAX.
• Experience defining reward functions for complex robotic tasks.

✨ Nice to Have

• Experience with sim-to-real transfer and domain randomization.
• Familiarity with other physics simulators like Isaac Gym or PyBullet.
• Published research in robotics, RL, or embodied AI.

🎁 Benefits & Perks

• 🏖️ Fully remote — work from anywhere on accepted locations list.
• 💰 Weekly payment via PayPal or Stripe.
• 🕐 Flexible hours (15-40+ hrs/week).

📨 Hiring Process

Estimated timeline: 2-3 weeks · AI estimate

1. 1Application review· 1 week
2. 2Technical interview· 1 hour
3. 3Offer· 1 week

🚩 Heads Up

• Hours vary and are not guaranteed; suggests keeping other work open.
• Majority of projects run at ~$30/hr, lower end of range.