Field AI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems that address complex challenges in robotics and unlock the potential of embodied intelligence.
Requirements
- Research experience in robot learning, reinforcement learning, imitation learning, or foundation models.
- Proficiency with Python and ML frameworks (PyTorch strongly preferred).
- Strong background in machine learning fundamentals and experimental design.
- Ability to analyze results rigorously and iterate quickly on research ideas.
- Published research in robotics, AI/ML, or related fields. (CoRL, ICRA, IROS, NeurIPS, ICML, CVPR, etc.).
- Experience with distributed training, large-scale ML systems, or high-performance computing.
- Hands-on experience with real robot platforms and sim-to-real research.
Responsibilities
- Conduct Research in Robot Skill Learning
- Investigate algorithms for learning transferable skills across diverse robot embodiments.
- Explore reinforcement learning, imitation learning, and multimodal foundation model approaches.
- Advance Foundation Models for Robotics
- Adapt VLMs and LLMs for robotics applications in perception, reasoning, and control.
- Work on methods for grounding language and vision models in real-world robot tasks.
- Design and run large-scale training pipelines in PyTorch and distributed systems.
Other
- Current MS or PhD student in Robotics, Computer Science, AI/ML, or a related field.
- A passion for robotics and embodied intelligence.
- Familiarity with ROS/ROS2 or other robot middleware.
- Contributions to open-source projects in robotics or AI.
- Background in perception (3D vision, mapping, traversability analysis) or dexterous manipulation.
