OR
OriginFounding AI Research Engineer - Robot Learning
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Before you apply
SalaryNo pay range posted
LocationSan Francisco, California, United States
Work modeon-site
Typefull-time
SeniorityLead
DepartmentComputer Visions & AI
Company size51–200 people
First seenSep 11, 2026 · 1mo ago
Verified live16h ago
At a glanceSummarised by Seekless from the posting.
BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots
Strong Python and PyTorch; 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 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
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
Skills
Python
PyTorch
VLA
imitation learning
reinforcement learning
vision-language models
robot learning from demonstration
sim-to-real
ROS2
Isaac Sim
TensorRT
ONNX
CUDA
OpenVLA
RT-2
Octo
DreamerV3
V-JEPA
Jetson AGX Orin
GELLO
SpaceMouse
VR
DAgger
HG-DAgger
About Origin
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 site.
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.
The Role
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. 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.
•
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.