# Founding AI Research Engineer - Robot Learning

**Company:** [Origin](https://hotfix.jobs/companies/origin-tech)
**Location:** San Francisco, CA
**Role:** AI Research
**Skills:** PyTorch, Python, Ros2, Isaac Sim, TensorRT, Onnx, CUDA, Imitation Learning, Reinforcement Learning, Vision-Language Models, Vla, World Models, Sim-To-Real
**Posted:** 2026-04-01

> Develops and deploys vision-language-action models and world models for autonomous robot finishing tasks in construction. Owns full lifecycle from data collection via teleoperation to edge deployment on Jetson hardware, requiring strong ML on robotics experience.

## Job Description

## 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.

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