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About the Role
Our system runs a Multi Agent Action Expert architecture, combining classical precision algorithms with learned policies. This role involves expanding learned components while ensuring system production safety. You will manage the entire lifecycle of learned components on OG-1, from data collection and model training to edge deployment on Jetson AGX Orin, with every research project having a deployment milestone.
Responsibilities
- Train and deploy VLA models for contact-rich manipulation using imitation learning infrastructure.
- Build the data flywheel, including teleoperation pipelines, online correction, and demonstration curation.
- Research and prototype world models for surface state prediction, spray dynamics, and anomaly detection.
- Design offline evaluation metrics to predict real-world finishing quality before deployment.
- Optimize models for edge deployment, including TensorRT compilation, latency profiling, and memory budgeting on dual Jetson AGX Orin.
- Design the interface for learned policies to propose actions and for deterministic safety layers to enforce constraints.
Requirements
- BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots.
- Strong Python and PyTorch skills; comfort modifying research codebases.
- Experience in at least two of: imitation learning, RL, vision-language models, robot learning from demonstration, sim-to-real.
- Track record of deploying ML on real hardware, including debugging policy failures on actual robots.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience working with Simulation Systems like Isaac Sim.
- GPU profiling and optimization (TensorRT, ONNX, CUDA); understanding of policy latency impact on contact control.
Skills
- Python
- PyTorch
- Imitation Learning
- RL
- Vision-Language Models
- Robot Learning from Demonstration
- Sim-to-Real
- ROS2
- Isaac Sim
- TensorRT
- ONNX
- CUDA
- VLA architectures
- Foundation model fine-tuning for robotics
- Teleoperation data collection
- DAgger/HG-DAgger pipelines
- World model architectures
- Construction domain
- Manufacturing domain
- Contact-rich industrial domains
Location
- New York City
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
Education Level
- BS
- MS
- PhD
About the Company
- Origin is building Physical AI for the built world, starting with autonomous robots for Interior Construction.
- We are building Construction Action Models which allows our modular robots to learn, adapt and work in unstructured construction job sites.
- Our robots are already deployed on live sites in New York City, helping accelerate schedules for large-scale commercial projects while improving safety and predictability on the job site.
- Backed by Tier-1 investors, Origin is working to close the gap between America’s surging demand for housing, data centers, and manufacturing infrastructure, and the construction industry’s growing labor shortage.