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About the Role
Define the technical roadmap for Robot Learning and Embodied AI. Build and deploy learned policies for real-world mobile manipulation and contact-rich tasks. This is not a lab position; every research project will have a deployment milestone.
Responsibilities
- Define the technical roadmap for Robot Learning and Embodied AI.
- Build and deploy learned policies for real-world mobile manipulation and contact-rich tasks.
- Develop imitation learning, reinforcement learning, VLA, and learning-from-demonstration systems.
- Fine-tune and adapt open-source VLA/foundation models for our robot platform.
- Build scalable teleoperation → dataset → training → evaluation → deployment loops.
- Develop DAgger / HG-DAgger and human-in-the-loop data collection pipelines.
- Build simulation environments and training pipelines using NVIDIA Isaac Sim / Isaac Lab.
- Develop sim-to-real strategies including domain randomization, system identification, and real-world policy adaptation.
- Explore world models and latent dynamics models for planning, prediction, and policy learning.
- Integrate learned policies with our existing ROS2 perception, planning, manipulation, control, and safety stack.
- Optimize inference for deployment on edge GPUs using TensorRT, ONNX, CUDA, profiling, quantization, and related techniques.
- Debug policies on physical robots: latency, observation drift, calibration errors, distribution shift, contact instability, action representation, control frequency, and hardware-induced failures.
- Establish rigorous evaluation for learned systems across simulation, replay datasets, and physical robot experiments.
- Build and mentor the Robot Learning team as we scale.
Requirements
- BS/MS/PhD in CS, Robotics, ML, or related field from a top university, or equivalent exceptional experience shipping learned systems on physical robots.
- PhD: minimum 2 years relevant experience. Without PhD: minimum 5 years relevant experience.
- Strong Python and PyTorch; comfortable modifying research codebases and open-source VLA implementations.
- Experience in at least two of: imitation learning, RL, VLA/VLMs, robot learning from demonstration, sim-to-real.
- Track record deploying ML on real robots — not just training policies, but debugging why they fail on actual hardware.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience with simulation systems such as NVIDIA Isaac Sim / Isaac Lab.
- GPU inference profiling and optimization (TensorRT, ONNX, CUDA); understand the impact of policy latency on real-time robot control.
Skills
- Python
- PyTorch
- ROS2
- NVIDIA Isaac Sim / Isaac Lab
- TensorRT
- ONNX
- CUDA
- Imitation Learning
- Reinforcement Learning
- VLA/VLMs
- Robot Learning from Demonstration
- Sim-to-Real
- Teleoperation
- DAgger / HG-DAgger
- World Models
- Latent Dynamics Models
- Contact-rich Manipulation
Location
- New York City
Work Type
- Full-time
Experience Level
- Minimum 2 years relevant experience (with PhD)
- Minimum 5 years relevant experience (without PhD)
Education Level
- BS/MS/PhD in CS, Robotics, ML, or related field
About the Company
- Origin is building Physical AI for the built world - starting with autonomous robots for Interior Construction.
- 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.