What to ExpectWe're looking for a Simulation Engineer to build and scale our physics simulation infrastructure. You'll work on sim-to-real transfer, domain randomization, and creating training environments that enable robots to learn in simulation and perform in the real world.
What You'll Do- Build and maintain simulation environments using MuJoCo, PyBullet, and Isaac Lab
- Develop sim-to-real transfer pipelines and domain randomization systems
- Create scalable infrastructure for parallel simulation execution
- Design RL training environments with realistic physics and diverse scenarios
- Collaborate with ML researchers on environment design for policy learning
What You'll Bring- 3-5 years of experience with physics simulation or robotics software
- Proficiency with MuJoCo, PyBullet, Isaac Lab/Sim, or similar engines
- Strong Python skills; familiarity with C++ for performance-critical code
- Experience with reinforcement learning training pipelines
- Understanding of robot dynamics, kinematics, and control
Nice to Have- Experience with sim-to-real transfer in deployed robotics systems
- Background in domain randomization and synthetic data generation
- Familiarity with GPU-accelerated simulation (Isaac Gym, Brax)
- Publications or projects in robot learning
We believe diverse teams build better products. Even if you don't meet every requirement listed, we encourage you to apply if you're excited about this role and our mission.
