Robot Learning Engineer

Light Speed Build Technologies is revolutionizing the construction industry through AI-powered robotics. Our flagship systems—BRUTE for automated wall panel manufacturing and DEX for on-site collaborative construction—are addressing the global housing crisis by delivering unprecedented speed, precision, and affordability in homebuilding.

As a Robot Learning Engineer, you will teach our robots to perform complex manipulation tasks from human demonstration. You will own the path from demonstration to deployed behavior: building the teleoperation systems that capture how skilled people do the work, turning that data into trained policies, and proving those policies on real robots with dexterous hands and custom tooling. You'll also shape how we learn from video of people doing the work itself.

This role is hands‑on, fast‑moving, and measured by one thing: robots that reliably do the job.

• Build and improve teleoperation pipelines using leader arms for bulk arm motion and VR hand tracking for fine, finger-level interaction
• Retarget human hand motion to a dexterous robot hand, developing in simulation first and validating on hardware
• Design data collection protocols so demonstrations are consistent, well‑structured, and train well
• Define what data is worth collecting and how much, and guide technicians who run collection sessions

• Train and fine‑tune manipulation policies using imitation learning and vision‑language‑action (VLA) models
• Choose policy architectures and action representations suited to multi‑step, contact‑rich tasks
• Combine teleoperation data with human demonstration video to improve policies
• Iterate quickly from data to trained policy to real‑robot trial

• Define and run evaluation protocols on real robots, measuring success rate per task step
• Diagnose failures across data, model, and hardware, and fix the right one
• Deploy policies on robots with ROS2‑based control and optimize inference for real‑time performance
• Work with integration engineers to run policies across a full task sequence

• Define dataset formats and data quality standards for training
• Supervise external partners who build data pipeline infrastructure and label human demonstration video, and set specs and accept deliveries
• Prototype extraction of useful training signals from egocentric (head‑mounted camera) video in‑house before scaling it out

• 3+ years of hands‑on experience in robot learning, ML for robotics, or closely related production ML
• Experience training policies from demonstrations on physical robots using imitation learning (e.g., ACT, Diffusion Policy, or similar)
• Experience collecting teleoperation or demonstration data for robot learning
• Strong PyTorch skills and experience running training, evaluation, and debugging end to end
• Track record of evaluating models in the real world and diagnosing failures

• Strong Python; working C++
• Experience with ROS or ROS2 and real robot hardware
• Understanding of manipulator kinematics and control fundamentals
• Proficiency with Linux, Docker, and Git

• MS or PhD in Robotics, Machine Learning, Computer Science, or a related field
• Experience fine‑tuning VLA or foundation models for robot control
• Experience with dexterous hands, VR hand tracking, or motion retargeting
• Experience building or using leader‑follower teleoperation systems
• Experience learning from human video or egocentric data
• Experience building and maintaining large‑scale robotics datasets
• Robotics simulation experience with Mu Jo Co , Isaac Sim, or similar
• Experience with robot learning datasets and…