Founding AI Research Engineer - Robot Learning at Origin

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

04 Jul, 26

Salary

0.0

Posted On

05 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Robot Learning, Python, PyTorch, Imitation Learning, Reinforcement Learning, Vision-Language Models, ROS2, Isaac Sim, TensorRT, CUDA, Edge Deployment, Jetson AGX Orin, Teleoperation, World Models, Robotics Middleware, Computer Vision

Industry

Robotics Engineering

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-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. Design the interface where learned policies propose actions and deterministic safety layers enforce constraints. 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.

Responsibilities

You will own the full lifecycle of learned components for autonomous robots, from data collection and model training to edge deployment on physical hardware. This involves building data pipelines, prototyping world models, and ensuring production-safe integration of learned policies with deterministic safety layers.