3-5 years of experience in physics simulation or robotics software
Proficiency with MuJoCo, PyBullet, Isaac Lab/Sim, or similar simulation engines
Strong Python programming skills; familiarity with C++ for performance-critical applications
Experience with reinforcement learning training pipelines
Understanding of robot dynamics, kinematics, and control systems
Responsibilities
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 reinforcement learning training environments with realistic physics
Collaborate with machine learning researchers to enhance environment design for policy learning
Benefits
Opportunity to work with a top-tier team from leading AI institutions
Engagement in cutting-edge research at the intersection of robotics and AI
Flexible work hours across global time zones
Encouragement of proactive communication and team collaboration
Inclusive culture that values diversity and innovation
Full Job Description
What to Expect
We'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.
