Senior Robot Learning Engineer
lightspeed photonicsNorthbrook (IL)
Senior Robot Learning Engineer
Posted yesterday
lightspeed photonicsNorthbrook (IL)
SENIORITY
Senior
About the role
About LightSpeed LightSpeed 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.
Position Overview:
As Senior Robot Learning Engineer, you will own how LightSpeed's robots learn and perform physical skills. You will build the data pipeline that captures how people do the work, the policies that learn from it, and the executor that runs every action on the robot, classical or learned, behind one interface.
Our robots do contact-rich, high-variance work: taping seams, routing cable, finishing drywall, setting fixtures. Your systems decide whether a new trade takes days or weeks to teach, and whether it runs reliably once it does.
What you'll work on Data and teleoperation - 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
Policies and training - 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
Execution and control - 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
Foundation and skill pipeline - 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
Technical leadership - 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
Required qualifications:
Robot learning and control - 5+ years in robotics or machine learning, including learned manipulation policies deployed on real hardware
- Deep experience with imitation learning and reinforcement learning, and with when to use each
- Force and impedance control for contact-rich manipulation
- Built a data pipeline for robot learning: teleoperation capture, labelling, versioning and evaluation
- Deep learning in PyTorch, from dataset to deployed policy
Engineering and leadership - Strong Python and C++
- Experience with ROS2, Linux, Docker and CI/CD workflows
- A record of setting technical direction across teams and mentoring engineers
Preferred qualifications: MS or PhD in Robotics, Machine Learning, Computer Science or a related field
- Vision-language-action models or other robot foundation models
- Diffusion policies, ACT or similar visuomotor policy architectures
- Manipulation of deformable objects: cable, textile, tape
- Bimanual or dexterous manipulation
- Sim-to-real transfer with Isaac Sim, MuJoCo or similar
- Teleoperation systems: leader-follower arms, VR, egocentric capture
- Robot learning datasets in MCAP or rosbag
- Contact-rich assembly, finishing or construction tasks
- Background in construction, manufacturing or industrial automation
Why Join:
LightSpeed - Teach robots the trades that build homes and address the housing crisis
- Own robot learning end to end, from demonstration capture to deployed policy
- Shape the foundation every trade is built on
- Work on rare real-world problems: deformable materials, contact-rich finishing, learning from human demonstration on factory and field robots
- Hands-on culture with direct access to robots, sensors and manufacturing environments
- Competitive compensation and comprehensive benefits
Employment Relationship This position is at-will, meaning either you or the Company may terminate employment at any time, with or without cause or notice.
Equal Opportunity LightSpeed Build Technologies is an equal opportunity employer committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and experiences.
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