Graduate Research Internship in Robot Learning

Job Description

About company

Field AI is a pioneering company dedicated to transforming how robots interact with the real world. They specialize in building risk-aware, reliable, and field-ready AI systems that tackle the most complex challenges in robotics, aiming to unlock the full potential of embodied intelligence. Field AI distinguishes itself by moving beyond typical data-driven approaches or pure transformer-based architectures, charting a new course with solutions already deployed globally. These solutions are delivering real-world results and rapidly improving through real-field applications.

Why Join Field AI?
The company is committed to solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Their Field Foundational Models™ are setting new standards in perception, planning, localization, and manipulation, with a strong emphasis on explainability and safety for deployment.

Team and Culture:
Field AI offers the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. This team boasts a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from prestigious organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX. They are poised to achieve ambitious goals in the robotics sector.

Field AI is building the next robotics revolution, seeking innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. They value individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. The team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.

Location:
The company is headquartered in Irvine, Southern California, and has both US-based and global teammates. They foster a fun, close-knit team environment.

Diversity and Inclusion:
Field AI celebrates diversity and is committed to creating an inclusive environment for all employees. They evaluate candidates and employees based on merit, qualifications, and performance, without discrimination based on race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.

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Job description: Graduate Research Internship in Robot Learning

This Graduate Research Internship in Robot Learning at Field AI is tailored for MS and PhD students passionate about advancing the state of embodied intelligence. As an intern, you will work closely with Field AI’s research and engineering teams.

Core Focus:
The internship focuses on investigating novel methods for skill learning, foundation models in robotics, and large-scale training pipelines.

Key Opportunities:
This role provides a unique opportunity to develop research ideas, design experiments, and directly test your work on real robots, effectively bridging cutting-edge AI research with practical field deployment. Interns will also have the chance to contribute to publications in top-tier venues, open-source software, and collaborative projects that advance the robotics research community.

What You’ll Get To Do:

• Conduct Research in Robot Skill Learning:
• Investigate algorithms for learning transferable skills across diverse robot embodiments.
• Explore reinforcement learning (RL), imitation learning, and multimodal foundation model approaches.

• Advance Foundation Models for Robotics:
• Adapt Vision-Language Models (VLMs) and Large Language Models (LLMs) for robotics applications in perception, reasoning, and control.
• Work on methods for grounding language and vision models in real-world robot tasks.

• Large-Scale Training and Experimentation:
• Design and run large-scale training pipelines using PyTorch and distributed systems.
• Experiment with sim-to-real transfer, domain adaptation, and scaling laws for robot learning.

• Deploy and Validate on Real Robots:
• Test and validate research ideas on real robot platforms including manipulators, mobile robots, and more.
• Tackle crucial field robotics challenges such as traversability, locomotion, and dexterous manipulation.

• Collaborate and Publish:
• Partner with Field AI’s research team to drive forward projects with real-world impact.
• Contribute to publications in top-tier venues like CoRL, ICRA, NeurIPS, ICML, CVPR, etc., where appropriate.

What You Have (Required Qualifications):

• Current MS or PhD student in Robotics, Computer Science, AI/ML, or a related field.
• Demonstrated research experience in robot learning, reinforcement learning, imitation learning, or foundation models.
• Proficiency with Python and ML frameworks (PyTorch strongly preferred).
• A strong background in machine learning fundamentals and experimental design.
• Ability to analyze results rigorously and iterate quickly on research ideas.
• A deep passion for robotics and embodied intelligence.

The Extras That Set You Apart (Preferred Qualifications):

• Published research in robotics, AI/ML, or related fields in top-tier venues (e.g., CoRL, ICRA, IROS, NeurIPS, ICML, CVPR).
• Experience with distributed training, large-scale ML systems, or high-performance computing (HPC).
• Hands-on experience with real robot platforms and sim-to-real research.
• 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.

Compensation and Benefits:

• The salary range for this position is between $70,000 – $300,000 annually. The final salary is determined by an individual’s background, experience, and may vary considerably based on geographic location, job-related knowledge, and skills.
• While an on-site presence is valued, Field AI is open to exploring hybrid or remote options.