We're hiring Robotics Engineers to bring state-of-the-art manipulation methods into the GRID platform. You'll work at the frontier of autonomous manipulation, building production-grade policies that generalize across embodiments — from fixed-base robotic arms and mobile manipulators to humanoids. This is a role for engineers who want their research to ship. You'll move between training runs, simulation, and real hardware in customer environments.
Design methods, tools, and infrastructure to push forward the state of the art in robotics and foundation models.
Define research goals informed by practical engineering concerns.
Train, adapt, and deploy manipulation foundation models — vision-language-action (VLA) policies, world action models (WAMs), reinforcement learning, and agentic robot policies.
Build synthetic data generation pipelines on high-fidelity simulation platforms, and drive sim-to-real transfer.
Develop data collection, training, and evaluation pipelines as production-quality code: extensible, well-documented, and usable by engineers outside your team.
Run experiments end to end — experimental design, reusable implementations, model evaluation, and clear reporting of results.
Contribute to publications and open-source releases.
Partner with customers to take manipulation capabilities from prototype to deployed product.
Qualification
Bachelor's degree in Computer ScienceResearch experience in machine learningProgramming experience in Python/C++MS or PhD in RoboticsAbout General Robotics
Required
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.
Preferred
MS or PhD in Robotics, Computer Science, Computer Engineering, or a related technical field.
Direct research experience in robot learning, computer vision, or machine learning.
First-author publications at peer-reviewed AI and robotics venues (NeurIPS, ICML, ICLR, CVPR, ICRA, IROS, CoRL).
Hands-on experience with modern manipulation policies — VLAs, world action models, reinforcement learning, imitation learning, diffusion or flow-matching policies, or agentic robot policies.
Experience with high-fidelity simulators such as NVIDIA Isaac Sim, MuJoCo, ManiSkill, AirSim, or CARLA, including domain randomization and sim-to-real workflows.
Familiarity with modern perception stacks and sensing modalities: RGB and RGB-D cameras, LiDAR, 3D representations (point clouds, meshes), and segmentation, detection, and tracking.
Judgment about systems constraints — latency, throughput, compute budget, memory — and the ability to factor them into model and architecture choices.
