Robotics Engineer - Humanoids focus

General Robotics • Redmond, WA

9h • Onsite

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level