About the Role:
We are looking for strong candidates who have a background in robotics and machine learning, especially with experience in Reinforcement Learning, Whole-Body Control and Humanoid Locomanipulation. This role offers a unique mix of conducting research and deploying whole body humanoid models.
Responsibilities
Train and deploy RL/IL policies for loco-manipulation tasks that perform reliably in the real world, measured by field task success rate.
Design high fidelity simulation environments that advance sim-to-real transfer and reduce the gap between simulation training performance and real-world deployment, enabling faster iteration cycles.
Define research goals informed by practical engineering concerns.
Contribute to experiments, including designing experimental details, writing reusable code, running model evaluations, and organizing results.
Contribute to publications and open-sourcing efforts.
Partner with the robot deployment team to ship RL trained policies to production customer sites, owning the entire pipeline from research to deployment
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Research experience in machine learning, robotics, and computer vision.
Experience with developing robotics algorithms or machine learning models at scale.
Programming experience in Python/C++. Good understanding of deep learning frameworks like Pytorch or Jax.
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
Desired Qualifications
Master's/PhD in Robotics, Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Direct experience in robotics, computer vision, or machine learning research.
First author publications at peer-reviewed AI and robotics conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICRA, IROS, CORL).
Experience with high fidelity simulation platforms such as Isaac Lab, Mjlab, ManiSkill, MolmoSpaces etc.
Experience working with training and deploying policies for whole-body humanoid tasks - reinforcement learning, imitation learning, and classical approaches
Experience working with modern computer vision algorithms and sensors (RGB, RGB-D cameras, LIDAR.), 3D (meshes, point clouds, etc.), segmentation, tracking, detection.
Experience with domain randomization, reward shaping, and the engineering needed to bridge sim-to-real gap for humanoid policies
Experience working with real-world deployment of proprioceptive and visual humanoid policies
Good understanding of systems considerations and the ability to factor these into model choices.
About General Robotics:
General Robotics is building the intelligence grid for physical AI — the platform that makes any robot, from robotic arms to humanoids, genuinely intelligent. Headquartered in Redmond, Washington, we're venture backed, including by Accenture, who invested in General Robotics in 2026 to advance Physical AI-powered robotics in manufacturing and logistics, and we're also part of Microsoft's Startups Pegasus Program. Our team's work spans some of the most widely adopted robotics and AI research to come out of Microsoft Research, Google Research and DeepMind — including AirSim, PACT, ClimaX, Tensorflow Object Detection and VideoPoet.
Work Authorization
This role is open to candidates currently based in and authorized to work in the US.
Equal Opportunity Employer
General Robotics is an equal opportunity employer. We do not discriminate on the
basis of any status protected by applicable law.
Accommodations
If you need a reasonable accommodation during the application or interview
process, please contact: HR@GeneralRobotics.company