Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

We are offering a Summer 2026 internship in Robot Learning for students interested in advancing embodied intelligence through large-scale learning, foundation models, and real-world robotic deployment. As a research intern, you will work closely with FieldAI researchers and engineers to explore novel approaches to robot learning and autonomy, with a focus on scalable methods that generalize across tasks and embodiments.

This internship is designed for PhD students who want to connect cutting-edge AI research with practical robotics systems. You will have the opportunity to design experiments, develop learning pipelines, and validate ideas on real robotic platforms, contributing directly to FieldAI’s deployed autonomy stack.

What You Have:

• Current PhD student in Robotics, Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.
• Research experience in robot learning, reinforcement learning, imitation learning, or related areas.
• Strong foundation in machine learning fundamentals and experimental methodology.
• Develop multi-modal data collection platform for day/night robot navigation data collection
• Collect high-quality datasets for reproducible and comparable research and evaluation
• Summarize and publish learnings in high-quality robot research conference or journal
• Ability to work independently while collaborating effectively in a research environment.
• Strong interest in embodied intelligence and real-world robotics systems.

The Extras That Set You Apart

• Prior experience working with real robot platforms.
• Familiarity with ROS or ROS 2.
• Experience with large-scale or distributed training systems.
• Publications or open-source contributions in robotics or AI.
• Background in perception, planning, or control for robotics.
• Interest in bridging foundational research with deployed robotic systems.