Robot Learning Engineer
About Origin
Origin is building physical AI for the built world. Our robots autonomously finish building interiors at production quality. OG-1 is deployed on live NYC commercial construction sites today. Backed by Accel.
Our system runs a Multi Agent Action Expert architecture: classical precision algorithms orchestrated alongside learned policies. The job is systematically expanding the learned components while keeping the system production-safe.
The Role
You own the full lifecycle of learned components on OG-1: from data collection and model training through edge deployment on Jetson AGX Orin. Every research project will have a deployment milestone. This is not a lab position.
What You Will Do
- Train and deploy VLA models for contact-rich manipulation using our imitation learning infrastructure.
- Build the data flywheel: teleoperation pipelines (GELLO, SpaceMouse, VR), DAgger-style online correction, demonstration curation.
- Research and prototype world models for surface state prediction, spray dynamics, and anomaly detection.
- Design offline evaluation metrics that predict real-world finishing quality before deployment.
- Optimize models for edge: TensorRT compilation, latency profiling, memory budgeting on dual Jetson AGX Orin.
- Hands-on experience with world models, including building or working with predictive or generative environment models (e.g., latent dynamics, video prediction, or planning-oriented models)
- Design the interface where learned policies propose actions and deterministic safety layers enforce constraints.
Requirements
- BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots.
- Strong Python and PyTorch; comfort modifying research codebases (you'll work directly with open-source VLA implementations).
- Experience in at least two of: imitation learning, RL, vision-language models, robot learning from demonstration, sim-to-real.
- Track record deploying ML on real hardware: not just training to convergence, but debugging why the policy fails on the actual robot.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience working with Simulation Systems like Isaac Sim.
- GPU profiling and optimization (TensorRT, ONNX, CUDA); you understand why 200ms policy latency kills contact control.
Strong Plus
- Hands-on with VLA architectures (π0/π0.5, OpenVLA, RT-2, Octo) or foundation model fine-tuning for robotics.
- Teleoperation data collection and DAgger/HG-DAgger pipelines.
- World model architectures (DreamerV3, V-JEPA, latent dynamics models).
- Construction, manufacturing, or contact-rich industrial domains.
- Publications at CoRL, RSS, ICRA, NeurIPS: valued but equivalent shipped work counts.
Note - We’re ideally looking for candidates with 3–4 years of hands-on experience in robotics, machine learning, or applied AI systems.
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