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

MISSION

Research on world models and self‑play only moves as fast as the data and infrastructure underneath it. We need someone who can turn "we have robot fleet access" into a working pipeline: collecting rollouts, closing the sim‑to‑real loop, and making experiments reproducible and fast to run. You'd own that layer.

WHAT YOU'LL DO

• 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.

WHAT WE LOOK FOR

• 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.

NICE TO HAVE

• 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.