Senior Robot Learning Engineer

Jobverse.io; Northbrook (IL); On-site; USD 126,000 - 154,000; Full time

Job summary

LightSpeed Build Technologies is seeking a Senior Robot Learning Engineer to own learning and execution systems for its AI-powered robots. You will oversee demonstrations, data pipelines, policy training, and deployment, while guiding engineers and contributing hands-on production code and robot testing.

In this role you will shape technical architecture, mentor teams, and ensure safe, efficient operation of robotics systems across simulated and real-world environments.

Job description

LightSpeed Build Technologies develops AI-powered robotics for construction. The Senior Robot Learning Engineer owns the learning and execution systems for its robots, from demonstration data capture and policy training to control, evaluation, and deployment. The role also sets technical architecture and mentors engineers while remaining hands‑on with production code and robot testing.

Responsibilities

• Define how we capture demonstrations: teleoperation rigs (leader arms, VR) and egocentric capture of people doing the work
• Own the data pipeline: synchronized observations and actions, episode labelling, dataset versioning and quality checks
• Decide what data each new task needs, and how failures are labelled and fed back
• Partner with the world sensing principal on observation spaces and sensor streams
• Set the policy architecture: a shared vision and force backbone with task-conditioned heads, so a new skill needs a few demonstrations, not a new model
• Train with imitation learning, diffusion or transformer policies and vision-language-action models; use reinforcement learning where the reward can be clearly defined
• Build the training and evaluation loop, in simulation and on real robots, with held-out evaluations that gate deployment
• Solve manipulation of deformable materials: cable, tape, membrane
• Set coverage and first-pass yield targets per skill, and measure against them
• Own the actuate executor: classical control and learned policies behind one contract, so the planner can choose either for each step
• Build the compliance layer for contact-rich work: nailing, pressing, taping, mudding, insertion, with force and impedance control
• Build recovery behaviours for the common failures of each primitive
• Deploy policies on the robot within real-time and safety limits
• Define what each tool must sense and report, so its actions can be learned from and controlled
• Co‑own the foundation interfaces with the world sensing and agentic planning principals, and own the data and evaluation interface
• For each new trade: collect demonstrations, train and evaluate the policy, add recovery
• Drive down the time it takes to teach a new trade
• Set robot learning and control architecture across both robot families
• Lead and mentor learning and manipulation engineers
• Make the system something engineers can teach new trades with, without you in the loop
• Stay hands‑on: write production code and test on real robots every week