About this RoleWe'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.
Responsibilities- 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.
Minimum Qualifications- 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.
Desired Qualifications- 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.
