Research Engineer - Sim-to-Real & Robot Learning Infrastructure

_ Build and maintain the data pipeline connecting real robot rollouts to training infrastructure. _ Own sim-to-real transfer - closing the gap between simulated training and real hardware performance. _ Build tooling for large-scale training experiments: logging, evaluation harnesses, reproducibility, fast iteration loops. _ Work closely with our research scientists to translate architecture and algorithm ideas into running systems. _ Help shape engineering standards as one of the first hires - there’s no legacy codebase to inherit or work around.

_ Strong software engineering background with real experience in robotics, ML infrastructure, or simulation systems. _ Hands-on experience with at least one of: ROS/ROS2, robot simulation (Isaac Sim, MuJoCo, or similar), or large-scale ML training infrastructure. _ Comfortable working close to real hardware - debugging when something breaks on an actual robot, not just in simulation. _ Can move between “quick and dirty prototype” and “this needs to be reliable” depending on what the moment calls for., _ Experience with reinforcement learning pipelines specifically (not just supervised/imitation training infra). _ Background in sim-to-real transfer research or robot learning benchmarks. _ Experience standing up ML infrastructure at a very early-stage team (few or no existing systems to build on). _ Familiarity with physics simulators beyond a single ecosystem.