Robotics Research Intern, Embodied AI

Spring or Summer 2027

FieldAI 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. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.

As a Robotics Research Intern in Embodied AI, you will investigate fundamental research problems in robot behavior and learning, active perception, human-robot interaction, or at the intersection of these areas, with a focus on complex, unstructured environments.

You will work at the intersection of research and real-world deployment, developing new algorithms, validating them on physical robots, learning from field data, and contributing to publications at leading robotics and AI venues.

What You'll Do

Depending on your research background and interests, you may work on one or more of the following areas:

• Robot Behavior & Learning: Research methods for learning, adapting, and generating robot behaviors from demonstrations, interaction, experience, and real-world data, enabling robots to perform long-horizon tasks in dynamic and unstructured environments.

• Active Perception & Embodied Reasoning: Develop methods that enable robots to actively reason about what to inspect, where to observe, and what actions to take next based on task objectives, observations, uncertainty, and environmental context.

• Human-Robot Interaction: Develop approaches for intuitive human-robot collaboration, including natural task specification, interactive robot behaviors, human-in-the-loop reasoning, and adaptation to user intent and feedback.

• Embodied AI Models: Investigate and adapt foundation models, including vision-language models, vision-language-action models, and world models, for reasoning, planning, interaction, and decision-making on physical robots.

• Real-Robot Experimentation: Implement and evaluate research algorithms on robotic platforms operating in realistic, unstructured environments, and use deployment data to iteratively improve the underlying methods.

• Research & Publication: Conduct cutting-edge research in embodied AI and robotics, design rigorous experiments and evaluations, and contribute to publications at leading robotics and AI conferences and journals.

What You Bring

• Currently pursuing or recently completed a PhD in Robotics, Computer Science, Aerospace, Electrical Engineering, Mechanical Engineering, or a related field.

• Strong research foundation in one or more areas such as robot learning, planning and decision-making, embodied AI, perception, active perception, or human-robot interaction.

• Familiarity with state-of-the-art research in embodied AI and robotics.

• Strong programming skills in Python and/or C++.

• Experience with modern machine learning frameworks.

• Experience developing and evaluating algorithms for robotic or embodied systems.

• Strong analytical and problem-solving skills, with the ability to formulate research questions and design rigorous experiments.

• Strong written and verbal communication skills and the ability to collaborate across research and engineering teams.

What Will Set You Apart

• Hands-on robotics experience, including testing and deploying autonomous systems on physical robots. Experience with ROS or ROS2 and real robotic systems.

• Publications at leading AI and robotics venues such as CoRL, ICRA, IROS, RSS, NeurIPS, CVPR, or similar.

• Research experience in robot behavior and learning, such as imitation learning, reinforcement learning, learning from demonstrations, or learning from interaction and real-world experience.

• Research experience in active perception or active inspection, including task-driven perception, information-gathering behaviors, viewpoint selection, or reasoning under uncertainty.

• Experience in human-robot interaction, language-conditioned robotics, shared autonomy, or interactive robot learning.

• Experience fine-tuning or deploying large multimodal models, particularly vision-language, vision-language-action, open-vocabulary, or related foundation models.

Why Join FieldAI

FieldAI is tackling one of robotics’ hardest problems: deploying robots in unstructured, previously unknown environments. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.

You will work alongside a world-class team that values creativity, resilience, and bold thinking. We bring a decade-long track record of real-world deployments, strong performance in DARPA challenges, and experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX.

Be Part of the Next Robotics Revolution

Solving problems at this scale takes a team as unique as the mission. We are looking for people who push beyond conventional approaches, enjoy tackling tough and ambiguous questions, and bring interdisciplinary perspective. Our success depends on exceptional AI researchers and engineers, as well as strong software developers, product designers, field deployment experts, and communicators who can turn breakthroughs into real capability.

We are headquartered in Irvine, Southern California, with teammates across the US and around the world. Join us to shape the future of embodied intelligence as part of a fun, close-knit team building systems that work in the real world.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.