Description
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-protected.

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 setting 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.
Robust 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

📌 Robot Learning Engineer Bengaluru (India)
🏢 Origin
📍 India