Robot Learning Apprentice

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 1-2 years of hands-on experience in robotics, machine learning, or applied AI systems.

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