As a Robotics Engineer, you'll operate and test robot platforms, integrate sensors, manage data pipelines, and debug issues to improve model evaluation.
THE ROLE
We don't build robots — we test models on them. As the founding Robotics Engineer, you'll bring up commercial robot platforms (humanoids, arms, mobile bases), wire up the sensors and data pipelines our world models need, and run the experiments that tell us whether the model actually works in the real world
WHAT YOU'LL DO
- Bring up and operate off-the-shelf robot platforms (humanoids, manipulators, mobile bases) for model evaluation and data collection.
- Integrate cameras, depth sensors, IMUs, and other sensors needed for world-model rollouts and policy evaluation.
- Own the data pipeline from robot to training: logging, synchronization, calibration, and replay.
- Build the test rigs and benchmarks that tell us whether the model is improving over time.
- Keep the fleet running — debug failures across hardware, drivers, and integration code.
- Be the bridge between research and the physical world: translate "what the model needs" into "what the robot does."
MINIMUM QUALIFICATIONS
- Strong systems integration background — robots, drivers, sensors, real-time control.
- Hands-on with ROS/ROS2 (or equivalent), Python, and Linux at the systems level.
- Comfortable debugging across the stack: from kernel-level driver issues to ML pipeline bugs.
- Track record of standing up complex robot setups and keeping them running.
Preferred: experience with humanoids/manipulators from major vendors (Unitree, Figure, 1X, Agility, Franka, etc.), and prior work in a research lab where the robot was a means to an end rather than the end itself.
CompensationThe base pay range for this role is $250,000 – $450,000 per year.
About LumaLuma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
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