About this RoleWe'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.
Responsibilities- 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.
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- 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.
Work AuthorizationThis role is open to candidates currently based in and authorized to work in the US.
