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ML Engineer, Simulation Infrastructure
Build the simulation environments and infrastructure that support rapid iteration on hand-mocap models. The role covers physics environments, sim-to-real calibration, and data generation at scale.
What you'll do
- Construct hand-object interaction environments with physically accurate dynamics.
- Reduce the sim-to-real gap between policies trained in simulation and signal collected in the field.
- Scale simulation data production for research and training workloads.
- Collaborate with the AI and collection teams on new training and evaluation tasks.
- Prototype new sensor configurations in simulation prior to hardware implementation.
What we're looking for
- 4+ years of professional Python and/or C++.
- Experience building environments in MuJoCo, PyBullet, Isaac Sim, or similar.
- Experience improving the performance of physics simulators or rendering pipelines.
- Discipline around testing infrastructure that maintains correctness as the simulation stack scales.
- MS or PhD in CS or related field, or equivalent applied experience.
Nice to have
- Calibration algorithms (extrinsic, intrinsic) for robotics or vision.
- Differentiable simulation experience.