Senior Robotics SWE, System Identification & Modeling

Graduate-level coursework or research in system identification, optimal estimation, or adaptive control.

Experience with nonlinear system identification techniques (e.g., extended Kalman filters, particle filters, neural-network-based model learning).

Familiarity with physics simulators (MuJoCo, Drake, Isaac Sim) and sim-to-real transfer, including using identified parameters to calibrate simulation environments.

Experience with real-time embedded system development, RTOS, or bare-metal firmware.

Exposure to automation environments such as warehousing, manufacturing, or logistics.

Experience with safety-critical systems.

Familiarity with ROS/ROS 2 and standard robotics middleware.

Experience building data pipelines for collecting, storing, and analyzing robot telemetry at scale.