We're hiring Research Engineers to develop foundation models for GRID. You'll work across model architecture, large-scale training, reinforcement learning, simulation, and evaluation to build robotic behaviors that generalize across tasks, environments, and embodiments.
We value exceptional depth in one or more relevant areas; candidates are not expected to have experience across all of them.
Formulate research hypotheses and design rigorous experiments informed by deployment needs.
Develop and scale foundation models for robotic perception and action.
Leverage simulation and diverse datasets for pretraining, post-training, reinforcement learning, and evaluation.
Scale existing simulation platforms for data generation and reinforcement learning across heterogeneous scenes and tasks.
Advance generalization beyond demonstrated tasks and robust spatial understanding.
Improve sim-to-real transfer and reliability under real-world deployment constraints.
Develop Auto-Engineering methods for scalable and robust deployment.
Implement, evaluate, and clearly communicate research as reusable, production-quality work.
Qualification
Bachelor's degree in Computer ScienceFamiliarity with inverse kinematicsMS or PhD in Machine LearningFirst-author publications at NeurIPSExperience with large-scale pretrainingAbout General Robotics
Required
Bachelor's degree in Computer Science, Robotics, a related technical field, or equivalent practical experience.
Research or applied experience in machine learning, robotics, or computer vision.
Strong Python programming skills and experience with PyTorch.
Experience developing and evaluating machine-learning models or robotics algorithms.
Experience with at least one robotics simulation platform.
Familiarity with inverse kinematics, dynamics, and robotic manipulation.
Demonstrated research ability through publications, substantial research contributions, or equivalent industry work.
Must obtain and maintain work authorization in the country of employment.
Preferred
MS or PhD in Machine Learning, Robotics, Computer Science, or a related field, or equivalent industry research experience.
First-author publications at NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, or ICRA.
Hands-on experience with Vision-Language-Action models, world models, world action models, diffusion or flow-matching policies, transformers, reinforcement learning, or imitation learning.
Experience with large-scale pretraining, data-mixture design, post-training, or reinforcement learning.
Experience scaling simulation platforms such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or ManiSkill for learning and data generation.
Experience with sim-to-real transfer and evaluation on real robotic systems.
A track record of taking research from hypothesis through implementation and robust validation.
