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You will design and build simulation environments and real-time infrastructure to accelerate robot learning. Your work enables the AI team to iterate rapidly on research by reducing dependence on real robot evaluations, constructing realistic simulation environments, and scaling simulation data. You’ll help bridge the gap between simulated and real robot behaviors, prototype new hardware in simulation, and support evaluation and training tasks across teams.
Job Responsibility:
Construct diverse, physically realistic environments for NEO in simulation
Close the sim-to-real gap between policies trained in simulation and deployed on real robots
Scale up the production of simulation data for research usage
Collaborate with AI and teleoperation teams to develop new training and evaluation tasks and ensure they run performantly
Work with the hardware team to prototype new robot hardware and test it virtually in simulation
Requirements:
4+ years of experience programming in Python, C++, or similar languages
Experience building environments and benchmarks using robotics simulators such as MuJoCo, PyBullet, or Isaac Sim
Experience improving performance of physics simulators or OpenGL rendering pipelines
Experience with testing practices to maintain correctness of simulation stacks used for robot learning
Courage to tackle unsolved technical challenges with strong work ethic
Advanced degree (MS or PhD) in Computer Science or related field
Knowledge of extrinsic and intrinsic calibration algorithms for robotics
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